How the evolution of the Yen is impacting the different japanese sector and the HouseHolds¶
In this notebook, you will find the work of Thomas, Amaury, Luca and Alexandre.
The objective of this research is to comprehensively analyze the impact of the Japanese yen's fluctuations on various sectors of the Japanese economy, including household consumption, imports and exports, tourism, and the production of goods. By examining the exchange rates of the yen against major currencies such as the US dollar (USD), Chinese yuan (CNY), and the JXY index, this study aims to uncover the direct and indirect effects of currency movements on economic performance and societal well-being. The scope of the research encompasses a detailed exploration of Gross Domestic Product (GDP) correlations with exchange rates, the influence on household income and spending patterns, sectoral trade balances, and overall economic productivity. Utilizing a combination of Ordinary Least Squares (OLS), Vector Autoregression (VAR), and Nonlinear Autoregressive Distributed Lag (NARDL) models, this research strives to provide a holistic view of the yen's role in shaping Japan's economic landscape and offer insights for policymakers and stakeholders to make informed decisions.
Overview of the Japanese Economy and the Significance of the Yen¶
Japan, the world's third-largest economy, is renowned for its advanced technological prowess, strong manufacturing sector, and significant contributions to global trade. As a highly industrialized nation, Japan has a diverse economic structure, with key industries including automobile manufacturing, electronics, robotics, and precision machinery. The country also has a well-developed service sector, with significant contributions from finance, real estate, and tourism.
In recent years, Japan has faced several economic challenges, including an aging population, deflationary pressures, and slow GDP growth. To combat these issues, the Japanese government and the Bank of Japan have implemented various monetary and fiscal policies aimed at stimulating economic growth and achieving stable inflation. Central to these efforts is the management of the Japanese yen (JPY), the country's official currency.
The yen plays a crucial role in Japan's economy for several reasons:
Trade Competitiveness: As an export-oriented economy, Japan relies heavily on its trade balance. The value of the yen significantly impacts the competitiveness of Japanese goods in global markets. A weaker yen makes Japanese exports cheaper and more attractive to foreign buyers, boosting the country's trade surplus. Conversely, a stronger yen can make exports more expensive and less competitive.
Monetary Policy: The Bank of Japan (BoJ) actively uses the yen's exchange rate as a tool to influence economic conditions. Through measures such as quantitative easing and interest rate adjustments, the BoJ aims to control inflation and stimulate economic growth. The yen's value is often a reflection of these policy actions.
Investment Flows: The yen is considered a safe-haven currency, attracting investment during times of global economic uncertainty. This can lead to fluctuations in the yen's value based on investor sentiment and risk appetite. The movement of capital in and out of Japan impacts domestic financial markets and economic stability.
Consumer Purchasing Power: The yen's strength affects the cost of imports, influencing domestic prices and consumer purchasing power. A stronger yen can lower the cost of imported goods and services, benefiting consumers but potentially contributing to deflationary pressures.
Debt and Interest Rates: Japan has a high level of public debt, and the cost of servicing this debt is influenced by the yen's value and interest rates. Managing the yen's value is thus essential for maintaining fiscal health and ensuring sustainable debt levels.
In the current economic environment, characterized by global uncertainties and shifting trade dynamics, understanding the yen's impact on different sectors of the Japanese economy is crucial. This research aims to provide insights into how the yen's fluctuations affect Japan's economic performance and to identify strategies that can enhance economic resilience and growth.
Chapter 1 - Exchange rates ¶
Dollar/Yen monthly ¶
From https://www.investing.com/currencies/usd-jpy-historical-data
This is the data for the value of the yen in a monthly plan. This data represent how much Yen you can buy with 1 US dollar
| Date | Price | Open | High | Low | Vol. | Change % | |
|---|---|---|---|---|---|---|---|
| 639 | 1971-02-01 | 357.56 | 357.56 | 357.56 | 357.56 | NaN | -0.04% |
| 638 | 1971-03-01 | 357.42 | 357.42 | 357.42 | 357.42 | NaN | -0.04% |
| 637 | 1971-04-01 | 357.40 | 357.40 | 357.40 | 357.40 | NaN | -0.01% |
| 636 | 1971-05-01 | 357.40 | 357.40 | 357.40 | 357.40 | NaN | 0.00% |
| 635 | 1971-06-01 | 357.40 | 357.40 | 357.40 | 357.40 | NaN | 0.00% |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 4 | 2024-01-01 | 146.88 | 140.84 | 148.82 | 140.81 | NaN | 4.13% |
| 3 | 2024-02-01 | 149.98 | 146.90 | 150.90 | 145.89 | NaN | 2.11% |
| 2 | 2024-03-01 | 151.31 | 149.98 | 151.98 | 146.48 | NaN | 0.89% |
| 1 | 2024-04-01 | 157.80 | 151.32 | 160.04 | 150.81 | NaN | 4.29% |
| 0 | 2024-05-01 | 156.74 | 157.74 | 158.04 | 151.87 | NaN | -0.67% |
640 rows × 7 columns
This is the data from 1970 to now, we can clearly see the change of character here
If we take a look on a smaller period we see that the Yen is loosing strength
Yen index (JXY) ¶
from https://www.investing.com/
The JXY is the index representing the Yen value, it is calculated based on the exchange rate of the yen between every currency.
| Date | Price | Open | High | Low | Vol. | Change % | |
|---|---|---|---|---|---|---|---|
| 206 | 2007-03-01 | 84.92 | 85.30 | 86.67 | 84.45 | 6.72M | 0.35% |
| 205 | 2007-04-01 | 83.72 | 84.94 | 85.10 | 81.85 | 2.27M | -1.41% |
| 204 | 2007-05-01 | 82.17 | 83.87 | 83.93 | 82.03 | 2.45M | -1.85% |
| 203 | 2007-06-01 | 81.24 | 81.99 | 82.79 | 80.61 | 2.66M | -1.13% |
| 202 | 2007-07-01 | 84.34 | 81.69 | 84.44 | 81.03 | 7.20M | 3.82% |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 4 | 2024-01-01 | 63.03 | 65.42 | 65.54 | 62.37 | 2.71M | -4.22% |
| 3 | 2024-02-01 | 61.78 | 63.29 | 63.53 | 61.40 | 2.50M | -1.98% |
| 2 | 2024-03-01 | 61.23 | 61.59 | 63.15 | 61.01 | 3.78M | -0.89% |
| 1 | 2024-04-01 | 58.70 | 61.24 | 61.29 | 58.65 | 4.15M | -4.13% |
| 0 | 2024-05-01 | 58.98 | 58.70 | 60.74 | 58.69 | 69.50K | 0.48% |
207 rows × 7 columns
Chinese yen / Yen ¶
from https://www.investing.com/currencies/cny-jpy-historical-data
| Date | Price | Open | High | Low | Vol. | Change % | |
|---|---|---|---|---|---|---|---|
| 386 | 1992-03-01 | 24.2891 | 24.2191 | 24.2891 | 24.2191 | NaN | 2.58% |
| 385 | 1992-04-01 | 24.1739 | 24.1043 | 24.1739 | 24.1043 | NaN | -0.47% |
| 384 | 1992-05-01 | 23.2560 | 23.1889 | 23.2560 | 23.1889 | NaN | -3.80% |
| 383 | 1992-06-01 | 22.9904 | 22.9282 | 22.9904 | 22.9282 | NaN | -1.14% |
| 382 | 1992-07-01 | 23.4504 | 23.3827 | 23.4504 | 23.3827 | NaN | 2.00% |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 4 | 2024-01-01 | 20.4905 | 19.8363 | 20.7332 | 19.8127 | NaN | 3.16% |
| 3 | 2024-02-01 | 20.8607 | 20.4939 | 20.9770 | 20.3183 | NaN | 1.81% |
| 2 | 2024-03-01 | 20.9562 | 20.8634 | 21.0982 | 20.3748 | NaN | 0.46% |
| 1 | 2024-04-01 | 21.7893 | 20.9541 | 22.0875 | 20.8482 | NaN | 3.98% |
| 0 | 2024-05-01 | 22.0688 | 22.2399 | 22.2898 | 21.5439 | NaN | 1.28% |
387 rows × 7 columns
The comparative analysis of the USD/JPY, JXY, and CNY/JPY forex prices reveals intriguing insights into the economic relationships and currency dynamics. From the graph, it is evident that the USD/JPY and CNY/JPY exchange rates exhibit similar trends over time. This similarity can be attributed to the close economic ties and trade relationships between Japan, China, and the United States. The synchronization in movement suggests that external economic factors, such as changes in global market conditions, trade policies, and economic growth rates, similarly impact these currency pairs.
On the other hand, the JXY (which typically refers to the J.P. Morgan Yen Index, representing the value of the yen relative to a basket of major world currencies) shows an inverse relationship compared to the USD/JPY and CNY/JPY pairs. This inverse movement can be explained by the nature of the JXY index itself. When the value of the yen strengthens against other currencies in the basket, the JXY rises, and conversely, when the yen weakens, the JXY falls. Therefore, when the USD/JPY and CNY/JPY rates increase (indicating a weaker yen relative to the USD and CNY), the JXY tends to decrease, reflecting a stronger yen against the broader basket of currencies.
Chapter 2 - GDP ¶
from https://www.esri.cao.go.jp/en/sna/data/sokuhou/files/2024/qe241/gdemenuea.html
The gdp is in quarterly real data seasonaly adjusted
| Date | GDP(Expenditure Approach) | |
|---|---|---|
| 0 | 1994-03-01 | 446,250.7 |
| 1 | 1994-06-01 | 443,807.5 |
| 2 | 1994-09-01 | 448,907.4 |
| 3 | 1994-12-01 | 447,170.6 |
| 4 | 1995-03-01 | 452,053.3 |
| ... | ... | ... |
| 116 | 2023-03-01 | 557,428.1 |
| 117 | 2023-06-01 | 563,122.6 |
| 118 | 2023-09-01 | 558,020.3 |
| 119 | 2023-12-01 | 558,040.5 |
| 120 | 2024-03-01 | 555,263.6 |
121 rows × 2 columns
GDP and exchange rates ¶
GDP vs USD/JPY ¶
GDP vs JXY ¶
The graph presents a comparative analysis of Japan's GDP (in blue) and the Japanese Yen Index (JXY, in red) over the period from 2007 to 2024. The left vertical axis represents GDP values, while the right vertical axis corresponds to the JXY values. Both data series are plotted quarterly, revealing the relationship between Japan's economic output and the yen's value against a basket of major currencies. Observations and Interpretation Inverse Relationship: General Trend: There is a noticeable inverse relationship between GDP and JXY. When the yen index (JXY) rises, indicating yen appreciation, the GDP tends to decline, and vice versa. Reason: A stronger yen makes Japanese exports more expensive and less competitive in international markets, potentially reducing export-driven income and GDP. Conversely, a weaker yen boosts export competitiveness, leading to higher GDP.
2008 Financial Crisis: Impact: The global financial crisis of 2008 is marked by a sharp decline in GDP. During this period, the yen appreciated significantly as investors sought safe-haven assets. Reason: The yen's appreciation during economic downturns is typical, as it is considered a safe-haven currency. However, this appreciation negatively impacted Japan's export-oriented economy, reducing GDP.
Post-2012 Recovery: Trend: Post-2012, the yen depreciated significantly, reaching a lower JXY value by 2014. During this period, GDP showed a gradual recovery. Reason: The yen's depreciation made Japanese goods cheaper abroad, boosting exports and economic growth. Policies such as Abenomics, introduced in 2012, aimed at monetary easing, fiscal stimulus, and structural reforms, also contributed to this recovery.
Recent Trends (2020-2024): Fluctuations: The graph shows significant fluctuations in both GDP and JXY around 2020, likely due to the COVID-19 pandemic's economic impact. Following the initial pandemic shock, there is a noticeable GDP recovery with a corresponding yen depreciation. Reason: The pandemic led to economic instability, causing sharp movements in both GDP and the yen's value. As Japan's economy started to recover, the yen depreciated, supporting export growth and boosting GDP. Long-Term Decline in JXY:
Observation: There is a long-term decline in the JXY index, indicating a gradual weakening of the yen over the past decade. Reason: Persistent monetary easing by the Bank of Japan, aimed at combating deflation and stimulating economic growth, has led to a weaker yen. This policy stance has helped maintain Japan's export competitiveness but has also resulted in prolonged yen depreciation.
GDP Analysis ¶
Hypothesis:
The Japanese Yen's strength inversely affects Japan's GDP due to its export-driven economy. A stronger yen increases the price of Japanese goods abroad, potentially decreasing demand and negatively impacting GDP.
Methodology
Approach: Employing correlation analysis and Ordinary Least Squares (OLS) regression to explore the relationship between the yen (JXY) and GDP, supplemented by Vector Autoregression (VAR) to examine dynamic interactions over time.
Strengths:
Correlation Analysis: Establishes a basic understanding of the relationship between JXY and GDP.
OLS Regression: Quantifies the impact of JXY changes on GDP, providing clear and direct results useful for economic interpretations.
VAR Model: Captures complex interactions and the time-lagged effects of JXY on GDP, enhancing the analysis by addressing variable interdependencies.
GDP correlation and OLS model ¶
Correlation between Government Consumption Expenditure and Yen Closing Value:
GDP JXY
GDP 1.000000 -0.739726
JXY -0.739726 1.000000
OLS Regression Results
==============================================================================
Dep. Variable: GDP R-squared: 0.547
Model: OLS Adj. R-squared: 0.540
Method: Least Squares F-statistic: 80.97
Date: Thu, 18 Jul 2024 Prob (F-statistic): 3.86e-13
Time: 18:54:04 Log-Likelihood: -750.48
No. Observations: 69 AIC: 1505.
Df Residuals: 67 BIC: 1509.
Df Model: 1
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
const 6.128e+05 9016.610 67.967 0.000 5.95e+05 6.31e+05
JXY -862.4253 95.845 -8.998 0.000 -1053.733 -671.118
==============================================================================
Omnibus: 12.229 Durbin-Watson: 0.488
Prob(Omnibus): 0.002 Jarque-Bera (JB): 12.809
Skew: -0.918 Prob(JB): 0.00165
Kurtosis: 4.042 Cond. No. 542.
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
Correlation Analysis The correlation matrix reveals a strong negative correlation between GDP and JXY, with a coefficient of -0.739726. This suggests that as the yen strengthens (higher JXY values), GDP tends to decrease, and vice versa.
Regression Analysis To further quantify this relationship, we conducted an Ordinary Least Squares (OLS) regression analysis, modeling GDP as a function of the JXY index. The regression equation is specified as: GDP=β0+β1×JXY Where: 𝛽0 is the intercept term. 𝛽1 is the coefficient for JXY.
Interpretation of Regression Results Intercept ( 𝛽 0 β 0 ): The intercept term is approximately 612,800, indicating the GDP level when JXY is zero. This value, however, is not interpretable in a practical sense since JXY cannot be zero. Coefficient ( 𝛽 1 β 1 ): The coefficient for JXY is -862.4253, implying that for each unit increase in the JXY index, GDP decreases by approximately 862.43 units. This negative relationship is statistically significant with a p-value of 0.000. The R-squared value of 0.547 indicates that approximately 54.7% of the variability in GDP is explained by the JXY index. The F-statistic is highly significant (p-value = 3.86e-13), further validating the model.
Discussion The inverse relationship between GDP and JXY can be attributed to Japan's export-driven economy. When the yen appreciates (higher JXY values), Japanese goods become more expensive for foreign buyers, reducing export volumes and negatively impacting GDP. Conversely, a weaker yen makes exports cheaper and more competitive, boosting economic growth. The 2008 financial crisis and the COVID-19 pandemic periods highlight this relationship. During these times, the yen appreciated as a safe-haven currency, coinciding with significant drops in GDP. The subsequent economic recoveries were accompanied by yen depreciation, supporting export growth and GDP recovery. Japan's monetary policy, particularly the Bank of Japan's efforts to combat deflation through monetary easing, has also played a role in maintaining a weaker yen to support economic growth. These policies, while effective in boosting exports, have led to prolonged yen depreciation.
GDP and VAR model ¶
We are now doing a Vector Autoregression (VAR) model to explore the dynamic interaction between JXY and GDP over time with a VAR model to capture the feedback loops between these variables.
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Thu, 18, Jul, 2024
Time: 18:54:04
--------------------------------------------------------------------
No. of Equations: 2.00000 BIC: 21.5009
Nobs: 65.0000 HQIC: 21.2173
Log likelihood: -854.020 FPE: 1.36475e+09
AIC: 21.0326 Det(Omega_mle): 1.11228e+09
--------------------------------------------------------------------
Results for equation GDP
=========================================================================
coefficient std. error t-stat prob
-------------------------------------------------------------------------
const 357.743419 1052.780817 0.340 0.734
L1.GDP -0.138479 0.126521 -1.095 0.274
L1.JXY -616.002686 265.627417 -2.319 0.020
L2.GDP 0.029065 0.127775 0.227 0.820
L2.JXY 238.405247 277.735618 0.858 0.391
L3.GDP -0.217054 0.124675 -1.741 0.082
L3.JXY -374.517691 269.945757 -1.387 0.165
=========================================================================
Results for equation JXY
=========================================================================
coefficient std. error t-stat prob
-------------------------------------------------------------------------
const -0.259367 0.498889 -0.520 0.603
L1.GDP -0.000038 0.000060 -0.634 0.526
L1.JXY 0.347753 0.125875 2.763 0.006
L2.GDP -0.000019 0.000061 -0.312 0.755
L2.JXY -0.260104 0.131613 -1.976 0.048
L3.GDP 0.000011 0.000059 0.181 0.857
L3.JXY 0.289545 0.127921 2.263 0.024
=========================================================================
Correlation matrix of residuals
GDP JXY
GDP 1.000000 0.070036
JXY 0.070036 1.000000
C:\Users\bodin\anaconda3\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency QS-DEC will be used. self._init_dates(dates, freq)
Conclusion of VAR Analysis and Forecast Integration The updated results from the Vector Autoregression (VAR) model substantiate the previously observed negative correlation between the Japanese yen (JXY) and Japan's GDP. The VAR model's coefficients reveal that significant changes in the JXY index are indeed influential on GDP. Specifically, the negative coefficient for JXY at lag 1 (-616.00) strongly supports the regression analysis indicating that a stronger yen, represented by higher JXY values, tends to depress GDP. This finding aligns with the economic principle that an appreciating yen can make Japanese exports more expensive and less competitive internationally, thereby potentially slowing economic growth.
Additionally, the positive coefficient for JXY at lag 2 (238.41) may suggest some delayed adjustment or other dynamic market reactions that mitigate the initial negative impact over time. However, another negative coefficient at lag 3 (-374.52) further confirms the overall adverse effect of yen appreciation on economic output in the longer term.
The correlation matrix from the VAR model, showing a low correlation coefficient of 0.070 between GDP and JXY residuals, indicates that while the relationship is statistically significant, it does not capture all the variance in GDP, highlighting the complexity and the presence of other influential factors beyond the yen's value.
These insights from the VAR model enhance our understanding from the initial correlation and simple OLS regression analyses. They provide a nuanced view of how the yen's value impacts GDP not just directly but also through its lagged effects. The VAR model's ability to account for these interactions over time adds considerable depth to our analysis, revealing the intricate dynamics between currency valuation and national economic performance.
Moreover, the historical context, including significant economic events such as the 2008 financial crisis and the COVID-19 pandemic, plays a crucial role in interpreting the fluctuations in GDP and JXY. These events align with the notable deviations captured in the model, underscoring the importance of considering external shocks in economic analysis.
In summary, the comprehensive approach employing OLS regression and VAR modeling elucidates the significant yet complex relationship between the yen's valuation and Japan's GDP. This robust analytical framework can inform further research and policy-making, aiming to leverage or stabilize Japan's economic indicators in relation to its currency dynamics.
Why those results : ¶
In the research paper from 1998 written by Alan C. Stockman, he highlights his discovery of the exchange rate 'not having a life of his own anymore' by finding a relation between the Exchange rate and real GDP. https://www.richmondfed.org/publications/research/economic_quarterly/1998/spring/stockman
"Contrary to this widely held contention, this article presents new evidence that exchange rates are connected with fundamentals, in particular with the relative gross domestic product (GDP) of each of the two countries involved, as predicted by nearly all exchange-rate theories. Moreover, they are related in the direction predicted by standard models: a country’s currency tends to be depreciated in real terms when that country’s real GDP is relatively high, and vice versa." p73-74
Chapter 3 - HouseHold consumption ¶
from https://www.esri.cao.go.jp/en/sna/data/sokuhou/files/2024/qe241/gdemenuea.html
The data are monthly real
| Date | GDP | USD_JPY | JXY | CNY_JPY | House | |
|---|---|---|---|---|---|---|
| 0 | 2007-03-01 | 527405.3 | 117.790000 | 84.920000 | 15.236400 | 285204.2 |
| 1 | 2007-06-01 | 527563.0 | 121.456667 | 82.376667 | 15.866400 | 286890.6 |
| 2 | 2007-09-01 | 524836.2 | 116.333333 | 85.886667 | 15.423767 | 284050.9 |
| 3 | 2007-12-01 | 527078.4 | 112.620000 | 88.686667 | 15.243233 | 284581.4 |
| 4 | 2008-03-01 | 528938.4 | 103.353333 | 96.760000 | 14.543133 | 285882.1 |
| ... | ... | ... | ... | ... | ... | ... |
| 64 | 2023-03-01 | 557428.1 | 133.026667 | 70.083333 | 19.408933 | 292284.8 |
| 65 | 2023-06-01 | 563122.6 | 139.980000 | 66.566667 | 19.733767 | 290275.4 |
| 66 | 2023-09-01 | 558020.3 | 145.720000 | 63.800000 | 20.139633 | 289394.5 |
| 67 | 2023-12-01 | 558040.5 | 146.973333 | 63.236667 | 20.451767 | 288235.6 |
| 68 | 2024-03-01 | 555263.6 | 149.390000 | 62.013333 | 20.769133 | 286208.3 |
69 rows × 6 columns
Households and JXY ¶
Observations The graph illustrates the relationship between Japan's household consumption (blue line) and the Japanese Yen Index (JXY, red line) from 2007 to 2024. A visual inspection reveals an inverse relationship between household consumption and the yen's value.
Key Points Inverse Relationship: Similar to GDP, household consumption tends to decrease when the yen appreciates (higher JXY values) and increase when the yen depreciates. Crisis Periods: During the 2008 financial crisis and the 2020 COVID-19 pandemic, there are notable drops in household consumption, coinciding with yen appreciation. Monetary Policy Effects: The Bank of Japan's monetary easing policies aimed at weakening the yen are reflected in the post-2012 period, where a weaker yen is associated with a gradual increase in household consumption. Explanations Export Dependency: A stronger yen reduces export competitiveness, leading to slower economic growth and reduced household income, thereby lowering consumption. Import Prices: A stronger yen makes imports cheaper, which can have a mixed effect on household consumption. While cheaper imports might increase consumption of imported goods, the overall economic slowdown due to reduced exports can still dampen household spending. Monetary Easing: Policies aimed at depreciating the yen (post-2012) stimulate economic growth, increase employment and income, thereby boosting household consumption. Crisis Impact: Economic uncertainty during crises leads to higher savings and lower consumption, while a stronger yen during these periods reflects its role as a safe-haven currency.
Research paper comparison ¶
We are now using two research paper about the impact of the national currency on the households consumption. It will permit us to compare the situation of Japan with other country where the study has already been done.
The studies on household consumption and exchange rate dynamics in African emerging economies (Uche et al., 2022) and BRICST countries (Derindag et al., 2022) collectively provide a comprehensive understanding of how exchange rate fluctuations impact household spending across different economic contexts.
In the African context, Uche et al. (2022) employ a modified non-linear ARDL and multiple threshold non-linear ARDL models to examine the effects of small and large exchange rate variations on household consumption. The study finds significant asymmetries in the response of household consumption to exchange rate movements. Specifically, in Algeria, Egypt, and Morocco, household consumption increases significantly with currency appreciation but decreases with depreciation. This implies that households in these countries are more responsive to positive exchange rate shocks. In contrast, in Kenya, Nigeria, and South Africa, household consumption remains largely unaffected by exchange rate changes, indicating a certain level of inelasticity in response to currency fluctuations
Similarly, Derindag et al. (2022) extend this analysis to the BRICST countries, employing the multiple asymmetric threshold non-linear ARDL (MATNARDL) model to differentiate between minor and major currency appreciations and depreciations. Their findings reveal that while the traditional NARDL model shows asymmetric effects in India and China only, the MATNARDL model indicates long-run asymmetries in all BRICST countries except India, and short-run asymmetries in all countries except Turkey. This suggests that in countries like Brazil, Russia, and China, household consumption is significantly influenced by both minor and major exchange rate changes, reflecting a more sensitive consumption pattern compared to Turkey.
Drawing a conclusion from both studies, it is evident that the impact of exchange rate movements on household consumption is context-specific and varies significantly across different regions and economic structures. In African emerging economies, household consumption in North African countries is highly sensitive to exchange rate appreciations, whereas in Sub-Saharan African countries, consumption remains stable regardless of exchange rate movements. In BRICST countries, the sensitivity to exchange rate fluctuations is more widespread, with notable asymmetries in response to both minor and major currency changes. These findings underscore the importance of tailored monetary and fiscal policies to manage the effects of exchange rate volatility on household consumption, ensuring economic stability and consumer confidence across different regions.
For detailed insights, refer to:
Household consumption and exchange rate extreme dynamics: Multiple asymmetric threshold non-linear autoregressive distributed lag model perspective (Uche et al., 2022).¶
https://onlinelibrary.wiley.com/doi/abs/10.1002/ijfe.2601
Exchange Rate Effect on the Household Consumption in BRICST Countries: Evidence from MATNARDL Model (Derindag et al., 2022).¶
https://www.worldscientific.com/doi/10.1142/S1793993322500107
NARDL model ¶
Further dive in the impact of the yen value on the Households consumption
(from https://onlinelibrary.wiley.com/doi/full/10.1002/ijfe.2601)
The Multiple Asymmetric AutoRegressive Distributed Lag (NARDL) model is an advanced econometric model used to examine the dynamic relationship between variables over time, capturing both short-term and long-term effects. It extends the traditional ARDL model to account for potential asymmetries in the relationships between variables, meaning it can capture situations where positive and negative changes in an independent variable may have different impacts on the dependent variable.
When applied to analyze the impact of exchange rates on household consumption, the NARDL model can provide insights into how changes in exchange rates (both appreciations and depreciations) affect household consumption patterns differently over time.
Augmented Dickey-Fuller test for d_log_House: Phillips-Perron test for d_log_House: Zivot-Andrews test for d_log_House: Augmented Dickey-Fuller test for d_log_GDP: Phillips-Perron test for d_log_GDP: Zivot-Andrews test for d_log_GDP: Augmented Dickey-Fuller test for d_log_JXY: Phillips-Perron test for d_log_JXY: Zivot-Andrews test for d_log_JXY:
C:\Users\bodin\anaconda3\Lib\site-packages\statsmodels\base\model.py:1906: FutureWarning: The behavior of wald_test will change after 0.14 to returning scalar test statistic values. To get the future behavior now, set scalar to True. To silence this message while retaining the legacy behavior, set scalar to False. warnings.warn(
NARDL Model Summary:
OLS Regression Results
==============================================================================
Dep. Variable: d_log_House R-squared: 0.756
Model: OLS Adj. R-squared: 0.727
Method: Least Squares F-statistic: 26.13
Date: Thu, 18 Jul 2024 Prob (F-statistic): 7.00e-16
Time: 18:54:04 Log-Likelihood: 223.92
No. Observations: 67 AIC: -431.8
Df Residuals: 59 BIC: -414.2
Df Model: 7
Covariance Type: nonrobust
==========================================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------------------
Intercept -0.0010 0.005 -0.214 0.831 -0.010 0.008
d_log_GDP 0.8255 0.070 11.726 0.000 0.685 0.966
pos_d_log_JXY 0.0184 0.053 0.344 0.732 -0.088 0.125
neg_d_log_JXY -0.0597 0.043 -1.374 0.175 -0.147 0.027
d_log_House.shift(1) -0.3397 0.118 -2.871 0.006 -0.576 -0.103
pos_d_log_JXY.shift(1) -0.0143 0.050 -0.284 0.777 -0.115 0.086
neg_d_log_JXY.shift(1) 0.0663 0.044 1.513 0.136 -0.021 0.154
d_log_GDP.shift(1) 0.1733 0.124 1.396 0.168 -0.075 0.422
==============================================================================
Omnibus: 16.015 Durbin-Watson: 2.129
Prob(Omnibus): 0.000 Jarque-Bera (JB): 48.657
Skew: 0.520 Prob(JB): 2.72e-11
Kurtosis: 7.043 Cond. No. 235.
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
Wald Test for Positive Shocks:
<F test: F=array([[0.10066733]]), p=0.752150366273299, df_denom=59, df_num=1>
Wald Test for Negative Shocks:
<F test: F=array([[2.10084304]]), p=0.1525127938951814, df_denom=59, df_num=1>
Coefficients:
Intercept -0.001001
d_log_GDP 0.825549
pos_d_log_JXY 0.018358
neg_d_log_JXY -0.059662
d_log_House.shift(1) -0.339679
pos_d_log_JXY.shift(1) -0.014310
neg_d_log_JXY.shift(1) 0.066321
d_log_GDP.shift(1) 0.173273
dtype: float64
RESULT explanation ¶
NARDL Model Summary:¶
The NARDL model results provide a detailed understanding of the relationship between household consumption and exchange rate dynamics, specifically the Japanese Yen Index (JXY), within the framework of both long-run and short-run dynamics. The following interpretation follows the standard economic equation from the referenced research paper.
(need to change this part to have a better understanding of the equations)
Long-Run Relationship¶
The long-run NARDL equation for household consumption (HC) can be expressed as:
[ \ln(HC_{kt}) = eta_0 + eta_1 \ln(ER_{kt}^+) + eta_2 \ln(ER_{kt}^-) + eta_3 \ln(NI_{kt}) + \epsilon_t ]
Where:
- ( \ln(HC_{kt}) ): Logarithmic value of household consumption expenditure.
- ( \ln(ER_{kt}^+) ): Positive partial sums of exchange rate changes (appreciation).
- ( \ln(ER_{kt}^-) ): Negative partial sums of exchange rate changes (depreciation).
- ( \ln(NI_{kt}) ): Logarithmic value of national income (GDP).
- ( eta_0 ): Constant term.
- ( eta_1 ), ( eta_2 ), ( eta_3 ): Coefficients for the respective variables.
- ( \epsilon_t ): Error term.
Based on the NARDL model results, we have the following coefficients:
- ( eta_0 ): 0.0023 (constant term, insignificant)
- ( eta_1 ) (ER⁺): 0.0042 (insignificant)
- ( eta_2 ) (ER⁻): -0.0586 (insignificant)
- ( eta_3 ) (GDP): 0.1815 (insignificant)
Short-Run Dynamics¶
The short-run NARDL equation, including lagged terms, can be expressed as:
[ \Delta \ln(HC_{kt}) = lpha_0 + \sum_{i=1}^{p} lpha_1 \Delta \ln(HC_{kt-i}) + \sum_{i=0}^{q} lpha_2 \Delta \ln(ER_{kt-i}^+) + \sum_{i=0}^{q} lpha_3 \Delta \ln(ER_{kt-i}^-) + \sum_{i=0}^{r} lpha_4 \Delta \ln(NI_{kt-i}) + \phi ECT_{t-1} + \epsilon_t ]
Where:
- ( \Delta \ln(HC_{kt}) ): Change in the logarithmic value of household consumption expenditure.
- ( \Delta \ln(ER_{kt-i}^+) ): Change in positive partial sums of exchange rate changes.
- ( \Delta \ln(ER_{kt-i}^-) ): Change in negative partial sums of exchange rate changes.
- ( \Delta \ln(NI_{kt-i}) ): Change in the logarithmic value of national income.
- ( lpha_0 ), ( lpha_1 ), ( lpha_2 ), ( lpha_3 ), ( lpha_4 ): Coefficients for the lagged terms.
- ( \phi ): Coefficient of the error correction term (ECT), indicating the speed of adjustment back to the long-run equilibrium.
- ( \epsilon_t ): Error term.
Based on the NARDL model results, we have the following coefficients for short-run dynamics:
- ( lpha_0 ): 0.002336 (constant term, insignificant)
- ( lpha_1 ) ((\Delta \ln(HC_{kt-1}))): -0.3426 (significant, p=0.008)
- ( lpha_2 ) ((\Delta \ln(ER_{kt-i}^+))): -0.0104 (insignificant)
- ( lpha_3 ) ((\Delta \ln(ER_{kt-i}^-))): 0.0608 (insignificant)
- ( lpha_4 ) ((\Delta \ln(NI_{kt-i}))): 0.1815 (insignificant)
(until here, or maybe remove everything)
Interpretation of NARDL Model Results¶
Long-Run Impact¶
In the long run, the model suggests that:
Positive Exchange Rate Changes (Appreciation):
- The coefficient (β₁ = 0.0184) is positive but statistically insignificant, indicating that appreciation of the JXY has a negligible long-run impact on household consumption.
Negative Exchange Rate Changes (Depreciation):
- The coefficient (β₂ = -0.0597) is negative but statistically insignificant, suggesting that depreciation of the JXY also has a negligible long-run impact on household consumption.
National Income (GDP):
- The coefficient (β₃ = 0.8255) is positive and statistically significant, indicating that changes in GDP have a substantial long-run impact on household consumption.
Short-Run Dynamics¶
In the short run, the model indicates:
Lagged Household Consumption:
- The coefficient (α₁ = -0.3397) is significant and negative, suggesting that previous periods' household consumption negatively impacts current consumption. This reflects a mean-reverting behavior where deviations from past consumption levels are corrected in the short run.
Positive Exchange Rate Changes (Appreciation):
- The coefficient (α₂ = -0.0143) is insignificant, indicating that short-term appreciations of the JXY have a negligible impact on household consumption.
Negative Exchange Rate Changes (Depreciation):
- The coefficient (α₃ = 0.0663) is insignificant, suggesting that short-term depreciations of the JXY also have a negligible impact on household consumption.
GDP:
- The coefficient (α₄ = 0.1733) is insignificant, indicating that short-term changes in GDP have a negligible impact on household consumption.
Wald Test Results¶
Positive Shocks:
- The Wald test for positive shocks shows an F-value of 0.1007 (p=0.752), indicating that positive exchange rate shocks do not significantly impact household consumption.
Negative Shocks:
- The Wald test for negative shocks shows an F-value of 2.1008 (p=0.153), suggesting that negative exchange rate shocks also do not significantly impact household consumption.
Summary¶
The NARDL model results indicate that variations in the Japanese Yen Index (JXY), both appreciations and depreciations, do not have a significant impact on household consumption in Japan in the long run or the short run. Household consumption appears to be relatively insensitive to exchange rate changes, focusing more on past consumption behaviors and potentially other macroeconomic factors not captured in this model, such as inflation, interest rates, and fiscal policies. The significant negative short-run coefficient for lagged household consumption suggests a mean-reverting behavior, indicating that deviations from past consumption levels are corrected over time.
Comparing the results ¶
| Variable | Japan | Algeria | Egypt | Kenya | Morocco | Nigeria | South Africa | Comment and Interpretation |
|---|---|---|---|---|---|---|---|---|
| Long-Run Coefficients | ||||||||
| ER⁺ (Positive Exchange Rate) | 0.0184 (not significant) | -0.017** (significant) | 0.008 (not significant) | 0.005 (not significant) | -0.010** (significant) | 0.003 (not significant) | 0.015 (not significant) | Positive exchange rate changes have a negligible impact in Japan. Negative effects observed in Algeria and Morocco indicate sensitivity to appreciation. |
| ER⁻ (Negative Exchange Rate) | -0.0597 (not significant) | -0.157** (significant) | 0.002 (not significant) | -0.067*** (significant) | 0.001 (not significant) | 0.031 (not significant) | 0.040 (not significant) | Negative exchange rate changes have a negligible impact in Japan. Significant negative effects observed in Algeria and Kenya indicate sensitivity to depreciation. |
| NI (National Income, GDP) | 0.8255 (significant) | 0.010 (not significant) | 0.022*** (significant) | 0.096*** (significant) | 0.067*** (significant) | 0.106*** (significant) | 0.226*** (significant) | GDP has a significant positive impact on household consumption in Japan and most African economies. |
| Short-Run Coefficients | ||||||||
| ΔER⁺ (Positive Exchange Rate) | -0.0143 (not significant) | -0.803*** (significant) | Not available | -0.251*** (significant) | -0.102** (significant) | 0.040 (not significant) | -0.546*** (significant) | Positive exchange rate changes have a negligible impact in Japan. Significant negative effects observed in multiple African economies indicate sensitivity to appreciation. |
| ΔER⁻ (Negative Exchange Rate) | 0.0663 (not significant) | -0.497*** (significant) | Not available | -0.081* (significant) | Not available | 0.039 (not significant) | -0.430** (significant) | Negative exchange rate changes have a negligible impact in Japan. Significant negative effects observed in multiple African economies indicate sensitivity to depreciation. |
| ΔNI (GDP) | 0.1733 (not significant) | 0.036 (not significant) | 0.602*** (significant) | 0.811*** (significant) | 0.911*** (significant) | 0.603*** (significant) | 0.457*** (significant) | Short-term changes in GDP significantly impact household consumption in most African economies, while it is not significant in Japan. |
| Wald Test for Asymmetry | ||||||||
| Positive Shocks | F-value = 0.1007 (not significant) | Significant | Not available | Significant | Not available | Not significant | Not significant | Positive exchange rate shocks do not significantly impact household consumption in Japan. Asymmetry observed in some African economies indicates varied responses to shocks. |
| Negative Shocks | F-value = 2.1008 (not significant) | Significant | Not available | Significant | Not available | Not significant | Significant | Negative exchange rate shocks do not significantly impact household consumption in Japan. Asymmetry observed in some African economies indicates varied responses to shocks. |
Summary¶
The comparative analysis highlights significant differences in the impact of exchange rate changes and GDP on household consumption between Japan and selected African economies. In Japan, exchange rate changes have negligible impacts on household consumption, both in the long and short run. In contrast, several African economies exhibit significant sensitivity to both positive and negative exchange rate changes, with notable long-run and short-run effects. GDP significantly influences household consumption in most African economies, while its impact in Japan is not significant. Asymmetry tests further reveal varied responses to exchange rate shocks in African economies, suggesting the need for context-specific economic policies.
Explanation with another source¶
The findings of this study align with those presented in Muellbauer and Murata's paper, "Consumption, Land Prices and the Monetary Transmission Mechanism in Japan" (Muellbauer & Murata, 2009). Their research highlights the weak impact of interest rate changes on consumption in Japan, suggesting that consumption in Japan is influenced more significantly by other factors such as land prices, fiscal policies, and the lack of significant household credit market liberalization. The paper documents how these economic conditions lead to a scenario where household consumption is less responsive to monetary transmission mechanisms, including exchange rate variations. This supports the conclusion that Japanese household consumption behavior is relatively insulated from exchange rate fluctuations, instead showing a strong reliance on historical consumption patterns and other macroeconomic variables not included in the NARDL model. These findings collectively help explain the insensitivity of household consumption to exchange rate changes observed in the Japanese context.
Households consumption details ¶
Conclusion from Research Paper¶
After comparing our findings with the research paper, we conclude that the exchange rate of JPY does not significantly impact overall household consumption. However, it is possible that the JPY exchange rate may influence specific components of household consumption rather than the aggregate expenditure.
Further Analysis¶
To explore this hypothesis, we will investigate whether particular categories of household expenditure are affected by changes in the JPY exchange rate. This section will focus on examining individual expenditure categories to determine if any are significantly influenced by fluctuations in the exchange rate.
Analysis of Household Expenditure Categories¶
We will analyze the following categories of household expenditure:
Food Expenses:
- Investigate if changes in the JPY exchange rate have a significant impact on food-related expenditures.
- Consider the effects of imported food prices and price elasticity.
Transportation Costs:
- Examine how fluctuations in the exchange rate influence transportation expenses, which may include fuel, public transit, and vehicle maintenance.
Housing and Utilities:
- Explore whether the JPY exchange rate affects housing costs and utility bills.
Healthcare Expenditures:
- Analyze if changes in the exchange rate impact healthcare spending, which might include medical services, pharmaceuticals, and insurance.
Education and Entertainment:
- Assess the effect of the exchange rate on spending related to education and entertainment activities.
By focusing on these specific categories, we aim to identify if the JPY exchange rate has a differential impact on various aspects of household consumption. This detailed analysis will provide insights into whether the exchange rate influences only certain components of household expenditure, thereby refining our understanding of its overall economic impact.
Data Collection and Time Range¶
The data used in this analysis is sourced from the Household Expenditure Survey provided by e-Stat, the government statistics portal site of Japan. This survey is conducted monthly and involves a sample of approximately 9,000 households across Japan. The survey collects detailed information on household income, expenditure, savings, and debt based on statistical theory and methods.
Data Details¶
- Survey Frequency: Monthly
- Sample Size: Approximately 9,000 households nationwide
- Survey Scope: Household income, expenditure, savings, and debt
- Data Range: Quarterly data from the year 2000 to 2023
The dataset includes quarterly observations spanning from 2000 to 2023, which allows for an in-depth analysis of trends and changes in household expenditure over more than two decades. This extensive time range provides a robust basis for examining the impact of various economic factors, including exchange rates, on household consumption patterns.
- Link to the dataset: https://www.e-stat.go.jp/dbview?sid=0003000800
For further details or to explore additional data, visit the e-Stat website.
| Year | Number of households distribution (sampling rate adjustment) [10,000 percent ratio] | Number of households [households] | Household members [people] | Employed personnel [person] | Age of household head [years] | Home ownership rate [%] | Percentage of households paying rent/ground rent [%] | Consumption Expenditure [Yen] | Food [yen] | ... | Furniture/household supplies (in kind) [yen] | Clothing and footwear (in kind) [yen] | Healthcare (in kind) [yen] | Transportation/Communication (in kind) [yen] | Education (in kind) [yen] | Educational entertainment (in kind) [yen] | Other consumption expenditures (in kind) [yen] | Engel coefficient [%] | Annual income [10,000 yen] | Adjusted number of households [households] | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2007 | 10000 | 8535 | 2.54 | 1.16 | 55.5 | 71.5 | 25.7 | 261526 | 59961 | ... | 250.0 | 316.0 | 55.0 | 218.0 | 0.0 | 551.0 | 261.0 | 22.9 | 553 | 1000000 |
| 1 | 2008 | 10000 | 8521 | 2.52 | 1.15 | 55.6 | 72.5 | 25.4 | 261306 | 60583 | ... | 239.0 | 275.0 | 37.0 | 206.0 | 0.0 | 545.0 | 233.0 | 23.2 | 547 | 1000035 |
| 2 | 2009 | 10000 | 8531 | 2.49 | 1.13 | 55.7 | 71.4 | 26.4 | 253720 | 59258 | ... | 231.0 | 240.0 | 47.0 | 194.0 | 1.0 | 514.0 | 211.0 | 23.4 | 535 | 1000063 |
| 3 | 2010 | 10000 | 8526 | 2.47 | 1.11 | 56.4 | 71.9 | 25.6 | 252328 | 58635 | ... | 197.0 | 217.0 | 48.0 | 163.0 | 0.0 | 474.0 | 211.0 | 23.2 | 521 | 999983 |
| 4 | 2011 | 10000 | 8365 | 2.47 | 1.09 | 56.9 | 71.0 | 26.3 | 247223 | 58376 | ... | 233.0 | 198.0 | 35.0 | 170.0 | 0.0 | 433.0 | 209.0 | 23.6 | 520 | 999968 |
| 5 | 2012 | 10000 | 8489 | 2.45 | 1.09 | 57.5 | 73.7 | 23.7 | 247651 | 58500 | ... | 185.0 | 213.0 | 44.0 | 176.0 | 0.0 | 382.0 | 172.0 | 23.6 | 515 | 999947 |
| 6 | 2013 | 10000 | 8478 | 2.44 | 1.09 | 58.0 | 75.4 | 22.3 | 251576 | 59375 | ... | 181.0 | 185.0 | 33.0 | 153.0 | 0.0 | 401.0 | 172.0 | 23.6 | 520 | 1000001 |
| 7 | 2014 | 10000 | 8467 | 2.41 | 1.07 | 58.3 | 74.7 | 23.1 | 251481 | 60272 | ... | 168.0 | 174.0 | 38.0 | 152.0 | 0.0 | 365.0 | 193.0 | 24.0 | 514 | 999947 |
| 8 | 2015 | 10000 | 8471 | 2.38 | 1.09 | 58.9 | 75.1 | 22.3 | 247126 | 61833 | ... | 157.0 | 166.0 | 28.0 | 197.0 | 0.0 | 343.0 | 158.0 | 25.0 | 515 | 999972 |
| 9 | 2016 | 10000 | 8400 | 2.35 | 1.08 | 59.0 | 75.7 | 21.8 | 242425 | 62248 | ... | 161.0 | 145.0 | 43.0 | 158.0 | 0.0 | 313.0 | 158.0 | 25.7 | 512 | 999990 |
| 10 | 2017 | 10000 | 8395 | 2.33 | 1.06 | 59.3 | 76.5 | 21.0 | 243456 | 62038 | ... | 141.0 | 118.0 | 21.0 | 135.0 | 0.0 | 314.0 | 133.0 | 25.5 | 510 | 999962 |
| 11 | 2018 | 10000 | 8319 | 2.33 | 1.08 | 59.3 | 75.8 | 21.6 | 246399 | 62819 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 25.5 | 511 | 1000037 |
| 12 | 2019 | 10000 | 8182 | 2.30 | 1.07 | 59.3 | 76.4 | 21.2 | 249704 | 63482 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 25.4 | 514 | 999946 |
| 13 | 2020 | 10000 | 8167 | 2.27 | 1.06 | 59.3 | 76.4 | 21.2 | 233568 | 63145 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 27.0 | 516 | 1000063 |
| 14 | 2021 | 10000 | 8088 | 2.25 | 1.06 | 59.4 | 74.4 | 22.2 | 235120 | 62531 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 26.6 | 514 | 999955 |
| 15 | 2022 | 10000 | 7999 | 2.22 | 1.05 | 59.5 | 75.0 | 22.2 | 244231 | 63597 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 26.0 | 525 | 999954 |
| 16 | 2023 | 10000 | 7909 | 2.20 | 1.05 | 59.5 | 75.9 | 21.3 | 247322 | 67078 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 27.1 | 517 | 999964 |
17 rows × 144 columns
| Quarter | Household number distribution (sampling rate adjustment) [10,000 percent ratio] | Number of households [households] | Household members [people] | Employed personnel [person] | Age of household head [years] | Home ownership rate [%] | Percentage of households paying rent/ground rent [%] | Consumption expenditure [yen] | Food [yen] | ... | Furniture/household supplies (in kind) [yen] | Clothing and footwear (in kind) [yen] | Health care (in kind) [yen] | Transportation/Communication (in kind) [yen] | Education (in kind) [yen] | Educational entertainment (in kind) [yen] | Other consumption expenditures (in kind) [yen] | Engel coefficient [%] | Annual income [10,000 yen] | Adjusted number of households [households] | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2000 1～3 | 10000 | 8633 | 2.71 | 1.28 | 51.9 | 66.3 | 31.1 | 276437 | 62112 | ... | 497.0 | 495.0 | 106.0 | 425.0 | 0.0 | 829.0 | 416.0 | 22.5 | NaN | NaN |
| 1 | 2000 4～6 | 10000 | 8644 | 2.70 | 1.29 | 52.0 | 65.8 | 30.8 | 279239 | 64645 | ... | 499.0 | 572.0 | 65.0 | 390.0 | 0.0 | 660.0 | 357.0 | 23.2 | NaN | NaN |
| 2 | 2000 7～9 | 10000 | 8629 | 2.69 | 1.28 | 52.4 | 66.2 | 30.6 | 275503 | 65595 | ... | 491.0 | 414.0 | 90.0 | 292.0 | 0.0 | 507.0 | 360.0 | 23.8 | NaN | NaN |
| 3 | 2000 10～12 | 10000 | 8648 | 2.68 | 1.28 | 52.7 | 67.9 | 29.9 | 292227 | 69070 | ... | 509.0 | 631.0 | 79.0 | 313.0 | 0.0 | 700.0 | 414.0 | 23.6 | NaN | NaN |
| 4 | 2001 1～3 | 10000 | 8611 | 2.68 | 1.28 | 52.7 | 67.1 | 29.8 | 274458 | 60349 | ... | 383.0 | 371.0 | 71.0 | 294.0 | 0.0 | 591.0 | 318.0 | 22.0 | NaN | NaN |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 91 | 2022 10～12 | 10000 | 7987 | 2.21 | 1.04 | 59.4 | 74.4 | 23.1 | 255388 | 68520 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 26.8 | 530.0 | 999925.0 |
| 92 | 2023 1～3 | 10000 | 7863 | 2.20 | 1.05 | 59.4 | 74.5 | 22.5 | 245524 | 62550 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 25.5 | 520.0 | 1000048.0 |
| 93 | 2023 4～6 | 10000 | 7905 | 2.20 | 1.06 | 59.4 | 74.7 | 22.1 | 238444 | 64773 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 27.2 | 510.0 | 999899.0 |
| 94 | 2023 7～9 | 10000 | 7903 | 2.19 | 1.06 | 59.4 | 74.0 | 22.8 | 241159 | 67853 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 28.1 | 524.0 | 1000145.0 |
| 95 | 2023 10～12 | 10000 | 7963 | 2.19 | 1.05 | 59.2 | 75.9 | 21.7 | 256267 | 71838 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 28.0 | 518.0 | 999748.0 |
96 rows × 144 columns
| Quarter | Price | |
|---|---|---|
| 0 | 2000Q1 | 12.922331 |
| 1 | 2000Q2 | 12.892431 |
| 2 | 2000Q3 | 13.024946 |
| 3 | 2000Q4 | 13.358829 |
| 4 | 2001Q1 | 14.379508 |
| ... | ... | ... |
| 91 | 2022Q4 | 19.781954 |
| 92 | 2023Q1 | 19.313777 |
| 93 | 2023Q2 | 19.656754 |
| 94 | 2023Q3 | 19.980654 |
| 95 | 2023Q4 | 20.395429 |
96 rows × 2 columns
Observations and Next Steps¶
As we can observe, these columns are non-seasonal. We will then perform a seasonal adjustment on them.
Correlation between original Consumption expenditure [yen] and Price: -0.4717
Impact of Price on Consumption Expenditure¶
Correlation Analysis¶
Our analysis reveals a correlation of -0.47 between Price and Consumption expenditure [yen], indicating a moderate negative relationship. This suggests that as prices increase, consumption expenditure tends to decrease, but the effect is not very strong.
Understanding the Correlation¶
Exchange Rate Impact: A rise in the CNYJPY exchange rate weakens the Yen, increasing theoretically import prices. Higher prices for imported goods can lead to reduced consumption as households adjust their spending.
Price Elasticity: Even though higher prices may lead to lower consumption expenditure the relationship is not perfectly represented since it depends on consummer behavior and price elasticity. Furthermore, we know that consumption is also influence by other factors such as income levels and consumer confidence
Visualization¶
The graph of normalized Consumption expenditure [yen] and Price over time shows that while there is some inverse relationship, the changes in price do not fully account for variations in consumption expenditure. This visual evidence supports the idea that price changes alone do not strongly dictate consumption patterns.
Conclusion¶
The correlation of -0.47 suggests that price increases have a noticeable but moderate impact on consumption expenditure. The concept of price elasticity indicates that while higher prices can lead to reduced spending, the effect is moderated by other factors influencing consumption behavior.
Analyzing the Impact of CNY/JPY Exchange Rate on Consumption expenditure of Households¶
Objective¶
The aim is to investigate whether categories of goods that are primarily imported into Japan experience price increases due to the appreciation of the Chinese Yuan (CNY) against the Japanese Yen (JPY). Furthermore, we want to assess whether these price changes affect consumer expenditure in these categories.
Hypothesis¶
Price Increase Hypothesis:
- Imported goods should exhibit price increases when the CNY appreciates against the JPY. This is because the cost of imported goods denominated in CNY becomes more expensive in JPY terms.
Expenditure Impact Hypothesis:
- Consumer expenditure in these imported categories may or may not be impacted by the price increases. Consumers have the option to adjust their consumption levels in response to price changes, potentially mitigating the overall expenditure impact.
Methodology¶
- Econometric Modeling:
- Using econometric models such as Vector Autoregression (VAR), we try to understand the dynamic relationship between the exchange rate, prices of imported goods, and consumer expenditure.
- Test for the significance of the exchange rate changes on prices and expenditures.
Expected Outcomes¶
Price Changes:
- Categories with a higher proportion of imported goods should show a significant correlation between the CNY/JPY exchange rate and price changes.
Consumer Expenditure:
- If consumer expenditure is inelastic, we might not see significant changes in expenditure despite price increases.
- If expenditure is elastic, consumers may reduce their consumption in response to higher prices, leading to a noticeable impact on expenditure.
By understanding the impact of the CNY/JPY exchange rate on imported goods and consumer behavior, we can gain insights into the broader economic implications of currency fluctuations
Analysis of the Impact of CNY/JPY Exchange Rate on Household Food Expenses in Japan¶
Introduction¶
In this analysis, we have examined the relationship between the Price variable (representing the CNY/JPY exchange rate) and Food [yen] (household expenses on food in Japan) using two time series models: the ARDL model and the VAR model. This section provides insights into whether and how the Price variable impacts Food [yen], and discusses potential improvements to the model.
Model Results¶
ARDL Model Results
==============================================================================
Dep. Variable: Food [yen] No. Observations: 95
Model: ARDL(4, 4) Log Likelihood -724.338
Method: Conditional MLE S.D. of innovations 692.850
Date: Thu, 18 Jul 2024 AIC 1470.675
Time: 18:54:06 BIC 1498.294
Sample: 06-30-2001 HQIC 1481.818
- 12-31-2023
=================================================================================
coef std err z P>|z| [0.025 0.975]
---------------------------------------------------------------------------------
const 19.0671 78.678 0.242 0.809 -137.477 175.611
Food [yen].L1 -0.3289 0.105 -3.144 0.002 -0.537 -0.121
Food [yen].L2 0.0665 0.113 0.589 0.557 -0.158 0.291
Food [yen].L3 0.1065 0.116 0.917 0.362 -0.125 0.337
Food [yen].L4 0.2197 0.109 2.019 0.047 0.003 0.436
Price.L0 27.0828 142.010 0.191 0.849 -255.472 309.638
Price.L1 375.1381 152.759 2.456 0.016 71.196 679.080
Price.L2 -0.7505 157.784 -0.005 0.996 -314.691 313.190
Price.L3 112.5853 156.691 0.719 0.475 -199.180 424.351
Price.L4 20.1981 147.168 0.137 0.891 -272.621 313.017
=================================================================================
C:\Users\bodin\AppData\Local\Temp\ipykernel_10956\930578837.py:5: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy analysis_df['Food [yen]'] = make_stationary(analysis_df['Food [yen]']) C:\Users\bodin\AppData\Local\Temp\ipykernel_10956\930578837.py:6: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy analysis_df['Price'] = make_stationary(analysis_df['Price']) C:\Users\bodin\AppData\Local\Temp\ipykernel_10956\930578837.py:9: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy analysis_df.dropna(inplace=True)
Food [yen] Price
Food [yen] 1.000000 0.068905
Price 0.068905 1.000000
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Thu, 18, Jul, 2024
Time: 18:54:06
--------------------------------------------------------------------
No. of Equations: 2.00000 BIC: 12.3919
Nobs: 94.0000 HQIC: 12.2951
Log likelihood: -835.548 FPE: 204756.
AIC: 12.2295 Det(Omega_mle): 192287.
--------------------------------------------------------------------
Results for equation Food [yen]
================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------
const 4.578411 77.670512 0.059 0.953
L1.Food [yen] -0.318413 0.096688 -3.293 0.001
L1.Price 324.632087 126.643573 2.563 0.010
================================================================================
Results for equation Price
================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------
const 0.056927 0.061030 0.933 0.351
L1.Food [yen] -0.000053 0.000076 -0.695 0.487
L1.Price 0.323492 0.099510 3.251 0.001
================================================================================
Correlation matrix of residuals
Food [yen] Price
Food [yen] 1.000000 -0.027335
Price -0.027335 1.000000
ARDL Model Results¶
Model Summary:
- Dependent Variable: Food [yen]
- Model: ARDL(4, 4)
- Significant Effects:
Food [yen].L1(p-value: 0.002) - Significant negative impactFood [yen].L4(p-value: 0.047) - Significant positive impactPrice.L1(p-value: 0.016) - Significant positive impact
Interpretation: The ARDL model results indicate that the
Pricevariable, particularly its lagged value (Price.L1), has a significant positive impact onFood [yen]. This suggests that past values of the CNY/JPY exchange rate have a notable effect on current household food expenses in Japan.
VAR Model Results¶
Model Summary:
- No. of Equations: 2
- Significant Effects:
L1.Food [yen](p-value: 0.001) - Significant negative impactL1.Price(p-value: 0.010) - Significant positive impact
Interpretation: The VAR model results show that the lagged value of
Pricealso has a significant positive effect onFood [yen]. This further supports the finding that changes in the CNY/JPY exchange rate influence household food expenses in Japan.
Conclusion¶
Based on the results from both the ARDL and VAR models, we can conclude that the Price variable (CNY/JPY exchange rate) significantly impacts Food [yen] (household food expenses in Japan). Specifically, an increase in the CNY/JPY exchange rate is associated with an increase in household food expenses. This relationship is significant and consistent across both models.
Price Elasticity of Food and Impact of Imports¶
The positive impact of the Price variable on Food [yen] can be better understood by considering the concept of price elasticity of food and the reliance of Japan on imported food products:
Price Elasticity of Food:
- Elasticity of Food: Food generally has a price elasticity of less than 1, meaning it is inelastic. This implies that changes in food prices have a relatively smaller effect on the quantity of food consumed. Consequently, even if food prices increase due to changes in the exchange rate, the expenditure on food may increase because households are less responsive to price changes in terms of reducing their consumption.
Impact of Food Imports:
- Imported Food Dependence: A significant portion of food consumed in Japan is imported
In 2022, Japan imported $24.9B in Foodstuffs, mainly from:
- China: $5.56B (22.3%)
- Thailand: $3.24B (13.0%)
- United States: $2.36B (9.5%)
- South Korea: $1.48B (5.9%)
- Italy: $1.44B (5.8%) The data are from OEC World.
. When the CNY/JPY exchange rate rises, the Japanese yen depreciates relative to the Chinese yuan. This depreciation makes imports more expensive.
- Explanation of Results: As the exchange rate rises and the yen weakens, the cost of imported food items increases. Since food consumption is relatively inelastic, households continue to purchase food despite higher prices. This results in an increase in overall food expenditure.
The observed positive relationship between the CNY/JPY exchange rate and household food expenses can thus be explained by the increased cost of imports leading to higher food prices. Given the inelastic nature of food demand, these price increases are not fully offset by reductions in food consumption, leading to a rise in expenditure on food.
How Price Impacts Food [yen]¶
The positive impact of Price on Food [yen] suggests that fluctuations in the CNY/JPY exchange rate affect the cost of food. As the exchange rate changes, it may influence the prices of imported food items or the overall cost structure of food expenses in Japan.
Potential Improvements¶
To enhance the analysis and model accuracy, the following improvements could be considered:
- Introduce Additional Variables:
- Inflation Rate: Including inflation data could help capture how general price level changes impact household expenses.
- Income Levels: Adding income data could provide insights into how changes in income influence food expenses in conjunction with exchange rate changes.
- Other Economic Indicators: Variables such as employment rates or consumer confidence indices might offer additional context for the relationship between exchange rates and food expenses.
By implementing these improvements, the analysis could offer a more comprehensive understanding of the factors influencing household food expenses in Japan and the role of the CNY/JPY exchange rate.
Analysis of the Impact of Exchange Rates on Service Expenditure¶
Context¶
In this analysis, we focus on the Service [Yen] category to examine if fluctuations in the CNY/JPY exchange rate impact this aspect of household consumption expenditure. Services represent a significant portion of household spending, making them an important component of our analysis.
Expectations¶
Exchange Rate Impact: Given that services are generally less sensitive to exchange rate changes compared to goods, we exepct no impact of the CNY/JPY exchange rate on service expenditure. The theoretical framework suggests that the prices of services are primarily determined by domestic factors rather than international exchange rates.
Model Setup: We use the ARDL (AutoRegressive Distributed Lag) model to analyze the relationship between service expenditure and the exchange rate. The ARDL model allows us to investigate both short-term and long-term dynamics between the variables.
ARDL Model Results
==============================================================================
Dep. Variable: Service [Yen] No. Observations: 96
Model: ARDL(4, 4) Log Likelihood -852.785
Method: Conditional MLE S.D. of innovations 2566.911
Date: Thu, 18 Jul 2024 AIC 1727.569
Time: 18:54:07 BIC 1755.309
Sample: 03-31-2001 HQIC 1738.765
- 12-31-2023
====================================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------------
const 1.766e+04 9903.918 1.783 0.078 -2044.361 3.74e+04
Service [Yen].L1 0.6157 0.109 5.629 0.000 0.398 0.833
Service [Yen].L2 0.3034 0.126 2.399 0.019 0.052 0.555
Service [Yen].L3 -0.2270 0.129 -1.760 0.082 -0.484 0.030
Service [Yen].L4 0.1315 0.114 1.158 0.250 -0.094 0.357
Price.L0 100.7470 542.340 0.186 0.853 -978.141 1179.635
Price.L1 154.8917 902.351 0.172 0.864 -1640.173 1949.956
Price.L2 -435.4816 927.126 -0.470 0.640 -2279.830 1408.867
Price.L3 514.0761 895.706 0.574 0.568 -1267.769 2295.921
Price.L4 -375.4342 535.125 -0.702 0.485 -1439.968 689.099
====================================================================================
C:\Users\bodin\AppData\Local\Temp\ipykernel_10956\2718644116.py:5: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy analysis_df['Service [Yen]'] = make_stationary(analysis_df['Service [Yen]']) C:\Users\bodin\AppData\Local\Temp\ipykernel_10956\2718644116.py:8: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy analysis_df.dropna(inplace=True)
ARDL Model Results¶
Interpretation¶
Impact of Lagged
Service [Yen]Values:- The coefficients for lagged
Service [Yen]values are significant for the first and second lags, indicating that past values ofService [Yen]have a strong influence on current values. - The positive coefficients for the first and second lags suggest a positive autoregressive effect, meaning that past expenditures on services are likely to affect current expenditures.
- The coefficients for lagged
Impact of
PriceonService [Yen]:- The coefficients for
Priceat different lags are not statistically significant (p-values are high), indicating that fluctuations in the CNY/JPY exchange rate do not have a measurable impact onService [Yen]. - The lack of significance in the
Pricecoefficients supports the initial hypothesis that service expenditures are not significantly influenced by exchange rate changes.
- The coefficients for
Model Fit and Stability:
- The model shows some variation in the coefficients over different lags, but the results are largely consistent with the theoretical expectation that
Service [Yen]is less sensitive to exchange rate fluctuations.
- The model shows some variation in the coefficients over different lags, but the results are largely consistent with the theoretical expectation that
Conclusion¶
The ARDL model results suggest that:
- Lagged Values: Previous expenditures on services are significant predictors of current service expenditure.
- Exchange Rate Impact: There is no significant impact of the CNY/JPY exchange rate on service expenditure. This aligns with the theoretical expectation that services, being less directly tied to international markets, are less influenced by exchange rate changes.
Service [Yen] Price
Service [Yen] 1.000000 -0.324948
Price -0.324948 1.000000
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Thu, 18, Jul, 2024
Time: 18:54:07
--------------------------------------------------------------------
No. of Equations: 2.00000 BIC: 15.2880
Nobs: 92.0000 HQIC: 14.9938
Log likelihood: -923.638 FPE: 2.66546e+06
AIC: 14.7946 Det(Omega_mle): 2.21159e+06
--------------------------------------------------------------------
Results for equation Service [Yen]
===================================================================================
coefficient std. error t-stat prob
-----------------------------------------------------------------------------------
const 18170.929700 9455.255671 1.922 0.055
L1.Service [Yen] 0.615444 0.108737 5.660 0.000
L1.Price 291.947708 516.479708 0.565 0.572
L2.Service [Yen] 0.303312 0.125737 2.412 0.016
L2.Price -506.765975 839.060262 -0.604 0.546
L3.Service [Yen] -0.231537 0.125867 -1.840 0.066
L3.Price 570.927684 836.889384 0.682 0.495
L4.Service [Yen] 0.132373 0.112740 1.174 0.240
L4.Price -405.055607 507.835065 -0.798 0.425
===================================================================================
Results for equation Price
===================================================================================
coefficient std. error t-stat prob
-----------------------------------------------------------------------------------
const 5.094374 1.924877 2.647 0.008
L1.Service [Yen] -0.000002 0.000022 -0.106 0.915
L1.Price 1.360398 0.105144 12.938 0.000
L2.Service [Yen] -0.000001 0.000026 -0.032 0.975
L2.Price -0.707559 0.170814 -4.142 0.000
L3.Service [Yen] -0.000045 0.000026 -1.770 0.077
L3.Price 0.564301 0.170372 3.312 0.001
L4.Service [Yen] 0.000009 0.000023 0.383 0.702
L4.Price -0.294018 0.103384 -2.844 0.004
===================================================================================
Correlation matrix of residuals
Service [Yen] Price
Service [Yen] 1.000000 0.020510
Price 0.020510 1.000000
Mean Salary in Japan vs HC ¶
We are now trying to find what really is influencing the consumption of Houseold, for that we are looking at the average salary in japan in the past 20 years and we will be trying to see if the correlation is better :
the datas are from : https://stats.oecd.org/Index.aspx?DataSetCode=AV_AN_WAGE# in NCU
In this case the data are nominal when the data for the households consumption is real
Correlation between House Consumption and Wage value:
House Salary
House 1.000000 -0.624681
Salary -0.624681 1.000000
Real mean salary¶
Here we are going to use the same data but trying to transform them in Real terms so that it will match better
In the data 2015 is the reference so it's 100 and the rest depends on 2015
Hypothesis Justification
Hypothesis: There is a positive correlation between mean salary and household consumption in Japan, indicating that higher incomes lead to increased consumption.
Methodology Justification
Approach: Employing correlation analysis and Ordinary Least Squares (OLS) regression to investigate the direct relationship between salary and consumption, complemented by contextual demographic analysis.
Strengths: Correlation Analysis: Provides a straightforward statistical measure to initially explore the relationship between salary and consumption. OLS Regression: Quantifies how changes in salary impact consumption, with statistical rigor to ascertain the significance of this relationship. VAR Model: Captures complex interactions and the time-lagged effects of mean salary on Households consumption, enhancing the analysis by addressing variable interdependencies.
Correlation between House Consumption and Wage value:
House Salary_real
House 1.000000 -0.748145
Salary_real -0.748145 1.000000
OLS Regression Results
==============================================================================
Dep. Variable: House R-squared: 0.560
Model: OLS Adj. R-squared: 0.555
Method: Least Squares F-statistic: 114.4
Date: Thu, 18 Jul 2024 Prob (F-statistic): 1.03e-17
Time: 18:54:09 Log-Likelihood: -937.01
No. Observations: 92 AIC: 1878.
Df Residuals: 90 BIC: 1883.
Df Model: 1
Covariance Type: nonrobust
===============================================================================
coef std err t P>|t| [0.025 0.975]
-------------------------------------------------------------------------------
const 5.09e+05 2.11e+04 24.154 0.000 4.67e+05 5.51e+05
Salary_real -0.0513 0.005 -10.697 0.000 -0.061 -0.042
==============================================================================
Omnibus: 5.093 Durbin-Watson: 0.454
Prob(Omnibus): 0.078 Jarque-Bera (JB): 4.455
Skew: -0.433 Prob(JB): 0.108
Kurtosis: 3.643 Cond. No. 1.37e+08
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 1.37e+08. This might indicate that there are
strong multicollinearity or other numerical problems.
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Thu, 18, Jul, 2024
Time: 18:54:09
--------------------------------------------------------------------
No. of Equations: 2.00000 BIC: 38.0117
Nobs: 88.0000 HQIC: 37.7764
Log likelihood: -1890.91 FPE: 2.17472e+16
AIC: 37.6176 Det(Omega_mle): 1.86604e+16
--------------------------------------------------------------------
Results for equation Salary_real
=================================================================================
coefficient std. error t-stat prob
---------------------------------------------------------------------------------
const -2835.136383 3611.989806 -0.785 0.432
L1.Salary_real 0.019152 0.111161 0.172 0.863
L1.House 0.865134 0.882051 0.981 0.327
L2.Salary_real -0.003971 0.111265 -0.036 0.972
L2.House 0.612046 0.928572 0.659 0.510
L3.Salary_real -0.043964 0.111508 -0.394 0.693
L3.House -1.119377 0.885121 -1.265 0.206
=================================================================================
Results for equation House
=================================================================================
coefficient std. error t-stat prob
---------------------------------------------------------------------------------
const 498.001737 455.624850 1.093 0.274
L1.Salary_real 0.017625 0.014022 1.257 0.209
L1.House -0.330358 0.111264 -2.969 0.003
L2.Salary_real 0.008159 0.014035 0.581 0.561
L2.House -0.025697 0.117132 -0.219 0.826
L3.Salary_real 0.001936 0.014066 0.138 0.891
L3.House -0.106053 0.111651 -0.950 0.342
=================================================================================
Correlation matrix of residuals
Salary_real House
Salary_real 1.000000 -0.162686
House -0.162686 1.000000
C:\Users\bodin\anaconda3\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting. self._init_dates(dates, freq)
Correlation and Regression Analysis Summary¶
The analysis of the relationship between mean salary and household consumption in Japan yields insightful results. Initially, the correlation between nominal salary and real household consumption shows a moderate inverse relationship with a coefficient of -0.62, implying that increases in nominal salaries tend to be accompanied by decreases in real household consumption. When adjusted for real terms, the correlation coefficient drops to -0.74, indicating a stronger negative relationship due to the adjustments for inflation effects.
Regression Model Details¶
An Ordinary Least Squares (OLS) regression model further explores this relationship, defined by the equation:
$$\text{Household Consumption} = 5.09 \times 10^5 - 0.0513 \times \text{Real Salary}$$
The model’s R-squared value of 0.560 explains approximately 56% of the variability in household consumption based on changes in real salary. The regression coefficient for real salary is -0.0513, which is statistically significant with a t-value of -10.697 and a p-value less than 0.0001. This suggests that for every unit increase in real salary, there is a corresponding decrease of 0.0513 units in household consumption.
VAR Model Analysis¶
A Vector Autoregression (VAR) model was also applied to further understand the dynamic relationship between mean salary and household consumption over time. The VAR model included three lags based on the optimal lag selection criteria.
Equation for Salary_real:
The coefficients for lags of real salary and household consumption were not statistically significant, indicating that past values of real salary and household consumption do not significantly predict current values of real salary.
The constant term was also not significant.
Equation for House:
The coefficient for the first lag of household consumption (L1.House) was significant with a value of -0.3304 and a p-value of 0.003, indicating that past household consumption negatively affects current household consumption.
The coefficients for lags of real salary were not statistically significant, suggesting that past values of real salary do not significantly predict current household consumption.
The VAR model’s results reinforce the findings from the OLS regression, emphasizing the negative relationship between real salary and household consumption.
Economic Implications¶
The negative coefficient for real salary indicates that higher real salaries lead to decreased household consumption, potentially due to increased savings or shifts in consumption patterns. This relationship highlights the critical role of real income adjustments in understanding household consumption dynamics and suggests significant behavioral changes in savings and investments as incomes rise in real terms.
-----------------------------------------------------------------------------------------------------------------------
Decrease in the Mean salary in Japan from 2000 to 2016 ¶
Findings from the Cabinet Office White Paper (2009)¶
The shift from regular to non-regular employment is a significant factor in wage dynamics during this period. The Cabinet Office's white paper highlights this transformation:
"The number of non-regular employees has increased and the percentage has risen to a third of the total employees. In particular, the number of regular employees had decreased and the number of temporary agency workers had increased in manufacturing industry, etc. from 2002 to 2007." (Page 19)
"Non-regular employees such as temporary agency workers are facing a significant risk such as high risk of disemployment." (Page 19)
The increase in non-regular employment, particularly in sectors like manufacturing, correlates with lower wage averages, as these positions typically offer lower salaries and less job security. This structural shift in the workforce composition directly impacts the overall mean salary.
Wage Gap and Income Inequality¶
Wage disparity and income inequality also widened during this period, exacerbated by the increasing prevalence of non-regular employment:
"Wage/household income (before redistribution) gap continues expanding. Trend in non-regular employment has contributed to the expansion of wage gap." (Page 22)
"The higher the ratio of non-regular employment is, the quicker the employment adjustment rate becomes." (Page 21)
The expanding wage gap is closely tied to the rise in non-regular employment, which often provides lower compensation compared to regular employment. This trend not only reduces average wages but also accelerates employment adjustments, meaning quicker layoffs and less stable employment conditions.
Economic Recession and Unemployment¶
Economic downturns during this period further exacerbated wage disparities and influenced mean salary trends:
"Increase in unemployment has a great impact on the expansion of income gap and relative poverty rate. Long-term unemployment leads to medium- to long-term expansion of wage gap due to career interruption." (Page 24)
Economic recessions contribute to higher unemployment rates, which disproportionately affect non-regular workers and those in unstable employment conditions. Long-term unemployment not only affects immediate income but also causes long-term career disruptions, hindering wage growth over time.
Conclusion¶
The decline in the average salary for full-time workers in Japan from 2000 to 2016 can be attributed to a combination of increased non-regular employment, wider wage gaps, and economic recessions. These factors collectively contributed to a labor market that favors lower wages and greater income inequality, impacting the economic stability of full-time employees across the country.
-----------------------------------------------------------------------------------------------------------------------
Why we obtain those results - Analysis of Economic Behavior ¶
Excess Savings and Consumption Patterns¶
The Annual Report on the Japanese Economy and Public Finance 2022 highlights an accumulation of excess household savings, particularly noted during the pandemic in 2020. It suggests:
"As consumer spending in the immediate future is supported by excess household savings, which increased substantially in 2020, a consumer spending recovery is expected to grow more robust under pay hikes." (p4)
This observation aligns with the regression findings where increased real salaries may not translate into proportional increases in consumption because households are leveraging these excess savings. This might be impacting the typical spending behaviors, possibly shifting from regular consumption to saving due to economic uncertainty or future financial planning.
Demographic Consumption Trends¶
The report also notes differences in spending habits among age groups:
"However, middle-aged and older people (aged between 40 and 59 and between 60 and 74) are more cautious of spending money on services than younger people aged between 25 and 39." (p2)
This cautious spending behavior among older demographics can contribute significantly to the observed negative correlation between salary and consumption, as these groups might be more inclined to save due to retirement concerns, as indicated in the 2017 report:
"Among the middle aged and elderly, amid an extension of the life expectancy, the necessity to prepare for a retirement may increase budget-minded households." (p6)
Labor Market and Income Dynamics¶
The Annual Report on the Japanese Economy and Public Finance 2016 and 2017 provides insights into labor market dynamics and their impact on consumption:
"Main factors include a fizzling out of demand for durable goods due to different policies and budget saving by households with householders under 39 years of age (young child-rearing households) and early 60's unemployed households." (p5, 2016)
"The weak growth in consumption reflects the followings: (1) Among young generation, the confidence for the future employment and income remains subdued." (p6, 2017)
These statements support the regression model's findings, suggesting that despite increases in nominal wages and employment, consumption growth has been sluggish. This could be due to a lack of confidence in long-term economic stability, leading to increased saving instead of spending.
Improvement in Employment/Income Environment¶
Conversely, improvements in the employment and income environment noted in the 2017 report have not fully translated into increased consumption:
"Improvement in employment/income environment (decline in unemployment rate, increase in employed persons, increase in nominal wages)... Private consumption has been picking up moderately. However, its growth has been slow compared to the substantial improvement in the employment and income environment." (p3, p5, 2017)
Conclusion¶
The data and government reports collectively suggest that the observed inverse relationship between real salary increases and household consumption can be attributed to several factors: heightened savings due to economic uncertainty, cautious spending among older demographics, and subdued confidence among the younger population regarding future economic prospects. These insights align with the regression findings and highlight the complex interplay between income levels, demographic factors, and consumption behaviors in Japan. This analysis underscores the importance of considering broader economic and demographic trends when interpreting statistical data on salary and consumption relationships.
House Price in Japan vs HC ¶
We still cannot explain why the households consumption was rising starting from 2000. We are going to make one last try at explaining the growth with free and open data. We are basing ourlself from the research paper from that we spoke about ealier about what influences the households consumption, he was saying that it could be more due to and also land prices. So for that we are going to use the Housing Prices index as we could not find free data on the land prices. The data are in real terms, seasonnaly adjusted from OECD :
https://data-explorer.oecd.org/vis?lc=en&pg=0&tm=house%20price&snb=47&vw=tl&df[ds]=dsDisseminateFinalDMZ&df[id]=DSD_AN_HOUSE_PRICES%40DF_HOUSE_PRICES&df[ag]=OECD.ECO.MPD&df[vs]=1.0&dq=JPN.Q.RHP.&pd=2000-Q1%2C2024-Q2&to[TIME_PERIOD]=false
C:\Users\bodin\AppData\Local\Temp\ipykernel_10956\1473902670.py:4: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
house_price['Date'] = pd.to_datetime(house_price['Date']).dt.strftime('%Y-%m-%d')
| Date | house_price | |
|---|---|---|
| 12 | 2000-03-01 | 128.227528 |
| 13 | 2000-06-01 | 127.379894 |
| 1 | 2000-09-01 | 126.298918 |
| 2 | 2000-12-01 | 125.445053 |
| 30 | 2001-03-01 | 124.048400 |
| ... | ... | ... |
| 70 | 2023-03-01 | 118.678510 |
| 71 | 2023-06-01 | 118.680353 |
| 72 | 2023-09-01 | 118.431648 |
| 73 | 2023-12-01 | 118.941428 |
| 3 | 2024-03-01 | 118.981919 |
97 rows × 2 columns
Hypothesis¶
There is a negative relationship between house prices and household consumption in Japan. As house prices increase, household consumption decreases due to a larger proportion of household income being allocated towards housing expenses, thereby reducing disposable income for other expenditures.
Methodology¶
Analytical Techniques:
Correlation Analysis: First, employ a correlation analysis to determine the strength and direction of the relationship between house prices and household consumption.
Ordinary Least Squares (OLS) Regression:
Use OLS regression to quantify the relationship between these variables.
Model house prices as a function of household consumption to observe the impact of consumption on housing prices.
Include necessary controls and dummy variables to account for other factors affecting house prices or consumption, such as interest rates, inflation, and economic cycles.
Vector Autoregression (VAR) Model:
Implement a VAR model to capture the dynamic interaction between house prices and household consumption over time.
Determine the optimal lag structure based on criteria like AIC, BIC etc...
Analyze the impulse response functions and variance decompositions to understand the magnitude and persistence of the effects between the variables.
Households consumption vs Houses prices index¶
Correlation and Regression Analysis¶
Correlation Matrix:
house_price house_change
house_price 1.000000 -0.554843
house_change -0.554843 1.000000
OLS Regression Results:
OLS Regression Results
==============================================================================
Dep. Variable: house_price R-squared: 0.308
Model: OLS Adj. R-squared: 0.300
Method: Least Squares F-statistic: 40.03
Date: Thu, 18 Jul 2024 Prob (F-statistic): 9.51e-09
Time: 18:54:09 Log-Likelihood: -316.69
No. Observations: 92 AIC: 637.4
Df Residuals: 90 BIC: 642.4
Df Model: 1
Covariance Type: nonrobust
================================================================================
coef std err t P>|t| [0.025 0.975]
--------------------------------------------------------------------------------
const 253.3259 23.418 10.817 0.000 206.801 299.851
house_change -1.5315 0.242 -6.327 0.000 -2.012 -1.051
==============================================================================
Omnibus: 3.671 Durbin-Watson: 0.117
Prob(Omnibus): 0.160 Jarque-Bera (JB): 2.323
Skew: 0.179 Prob(JB): 0.313
Kurtosis: 2.309 Cond. No. 2.84e+03
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 2.84e+03. This might indicate that there are
strong multicollinearity or other numerical problems.
VAR Model Results:
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Thu, 18, Jul, 2024
Time: 18:54:09
--------------------------------------------------------------------
No. of Equations: 2.00000 BIC: 0.889844
Nobs: 89.0000 HQIC: 0.656163
Log likelihood: -260.749 FPE: 1.64711
AIC: 0.498373 Det(Omega_mle): 1.41567
--------------------------------------------------------------------
Results for equation house_price
==================================================================================
coefficient std. error t-stat prob
----------------------------------------------------------------------------------
const 1.213599 5.575286 0.218 0.828
L1.house_price 1.328931 0.103456 12.845 0.000
L1.house_change -0.008412 0.079335 -0.106 0.916
L2.house_price -0.032201 0.171171 -0.188 0.851
L2.house_change -0.183010 0.082085 -2.230 0.026
L3.house_price -0.312915 0.109753 -2.851 0.004
L3.house_change 0.196505 0.072955 2.694 0.007
==================================================================================
Results for equation house_change
==================================================================================
coefficient std. error t-stat prob
----------------------------------------------------------------------------------
const 24.735175 7.880359 3.139 0.002
L1.house_price 0.418528 0.146229 2.862 0.004
L1.house_change 0.484163 0.112135 4.318 0.000
L2.house_price -0.448597 0.241941 -1.854 0.064
L2.house_change 0.284638 0.116022 2.453 0.014
L3.house_price 0.005783 0.155130 0.037 0.970
L3.house_change 0.003851 0.103117 0.037 0.970
==================================================================================
Correlation matrix of residuals
house_price house_change
house_price 1.000000 0.198138
house_change 0.198138 1.000000
Correlation Analysis¶
The correlation coefficient between house prices and household consumption is -0.554843, indicating a moderate inverse relationship. This suggests that as house prices increase, household consumption tends to decrease, possibly due to higher housing costs consuming a larger portion of household budgets, leaving less available for other consumption.
OLS Regression Analysis¶
The OLS model shows that household consumption negatively affects house prices with a coefficient of -1.5315, significant at the 0.000 level. The model's R-squared value is 0.308, meaning it explains about 30.8% of the variability in house prices. This supports the idea that increased household consumption could lead to a decrease in house prices, possibly through increased demand for more affordable housing options or shifts in housing market dynamics.
VAR Model Analysis¶
The VAR model further explores the dynamics between these two variables. The results for the house_price equation indicate a significant lagged relationship, with past values of house prices influencing current values. The coefficients for house_change are mostly insignificant, suggesting limited immediate impact from changes in household consumption on house prices within the given lags. However, the significant coefficients in the house_change equation suggest that house prices do have a noticeable impact on household consumption, especially in the lagged terms, illustrating the feedback loop between these variables.
Conclusion¶
The analyses collectively indicate a complex interaction between house prices and household consumption in Japan. While rising house prices appear to negatively impact household consumption, the feedback loop identified in the VAR model suggests that changes in consumption patterns can subsequently influence house prices, though the effect might lag over time. This relationship reflects the broader economic implications of housing market conditions on consumer behavior and overall economic health.
Global conlusion about Households consumption¶
The comprehensive analysis of household consumption in Japan, as examined through the lenses of exchange rate fluctuations, mean salary changes, and housing price movements, reveals nuanced insights into the economic behavior of Japanese households.
Exchange Rate Impact: The analysis suggests that both appreciations and depreciations of the Japanese Yen have minimal direct long-term effects on household consumption. Exchange rate fluctuations do not significantly influence household spending in the short or long run, indicating a relatively stable consumption pattern despite external economic shocks.
Salary and Consumption Dynamics: The relationship between mean salary and household consumption is notably inverse. Higher real salaries correlate with decreased household consumption, suggesting that increased income leads to higher savings rather than increased spending. This counterintuitive finding highlights a cautious consumer behavior potentially driven by long-term financial planning and risk aversion, which might be accentuated by Japan's aging demographic and their saving tendencies.
Housing Market Effects: The interaction between house prices and household consumption is moderately inverse. As housing prices increase, household consumption tends to decrease, likely due to a larger proportion of income being diverted towards housing expenses. This relationship is critical, especially in urban areas where high real estate prices can significantly constrain household budgets.
General Economic Conditions: The overall economic conditions, including GDP fluctuations, play a substantial role in shaping household consumption. The significant impact of GDP on consumption underlines the broader economic context in which these individual factors operate.
In conclusion, while exchange rate changes show limited direct effects, the combined dynamics of salaries and housing costs significantly influence household consumption patterns in Japan. These findings underscore the complex interplay between economic variables and consumer behavior, suggesting that policy measures focusing on income stability and housing affordability could have pronounced effects on boosting household consumption. Additionally, understanding these relationships helps in anticipating the effects of economic policies and market changes on consumer behavior, which is crucial for economic planning and forecasting.
Chapter 4 - IMPORTs AND EXPORTs ¶
We are now going to look at another part of the GDP : the import and export.
the datas are from : https://www.customs.go.jp/toukei/suii/html/time_e.htm
| Date | Exp-Total | Imp-Total | Net | |
|---|---|---|---|---|
| 0 | 1979-01-01 | 1192541404 | 1476341312 | -283799908 |
| 1 | 1979-02-01 | 1547063623 | 1474768659 | 72294964 |
| 2 | 1979-03-01 | 1924706473 | 1729013255 | 195693218 |
| 3 | 1979-04-01 | 1674210856 | 1754000207 | -79789351 |
| 4 | 1979-05-01 | 1795127553 | 1971518433 | -176390880 |
| ... | ... | ... | ... | ... |
| 538 | 2023-11-01 | 8817967103 | 9606294935 | -788327832 |
| 539 | 2023-12-01 | 9642926012 | 9584044267 | 58881745 |
| 540 | 2024-01-01 | 7332754388 | 9099294967 | -1766540579 |
| 541 | 2024-02-01 | 8249204131 | 8632181545 | -382977414 |
| 542 | 2024-03-01 | 9469324845 | 9082314865 | 387009980 |
543 rows × 4 columns
Imports & Exports with JXY ¶
Charts comparison with JXY¶
Hypothesis:
A stronger Japanese Yen leads to a decrease in both export volumes and import trade values.
Rationale:
A stronger yen increases the cost of Japanese goods abroad, potentially reducing export volumes.
Simultaneously, it decreases the local currency cost of imports, reducing the total trade value of imports despite stable volumes.
Approach:
Using correlation analysis and Ordinary Least Squares (OLS) regression to examine the impact of the yen's strength on import and export values.
Strengths:
Correlation Analysis: Identifies the direction and strength of relationships between the yen's value and trade metrics.
OLS Regression: Quantifies the impact of yen fluctuations on trade, providing clear, measurable outcomes.
Correlation between Net exports imports and JXY:
Imp-Total Exp-Total Net Price
Imp-Total 1.000000 0.817625 -0.609736 -0.680292
Exp-Total 0.817625 1.000000 -0.042192 -0.721713
Net -0.609736 -0.042192 1.000000 0.186978
Price -0.680292 -0.721713 0.186978 1.000000
OLS Regression Results
==============================================================================
Dep. Variable: Imp-Total R-squared: 0.463
Model: OLS Adj. R-squared: 0.460
Method: Least Squares F-statistic: 174.9
Date: Thu, 18 Jul 2024 Prob (F-statistic): 3.32e-29
Time: 18:54:10 Log-Likelihood: -4548.5
No. Observations: 205 AIC: 9101.
Df Residuals: 203 BIC: 9108.
Df Model: 1
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
const 1.204e+10 4.2e+08 28.697 0.000 1.12e+10 1.29e+10
Price -5.892e+07 4.46e+06 -13.224 0.000 -6.77e+07 -5.01e+07
==============================================================================
Omnibus: 3.273 Durbin-Watson: 0.230
Prob(Omnibus): 0.195 Jarque-Bera (JB): 3.316
Skew: 0.302 Prob(JB): 0.190
Kurtosis: 2.849 Cond. No. 538.
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
Regression Analysis¶
Dependent Variable: Import Total
Coefficient for JXY Price: -5.892e+07
This indicates that for every unit increase in the JXY Price, the total imports decrease by approximately 58.92 million units.
R-squared: 0.463
This value suggests that about 46.3% of the variability in Import Total can be explained by changes in the JXY Price.
Resume¶
| Metric | JXY Price Increases | JXY Price Decreases |
|---|---|---|
| Import Total | ↓ (Decrease in Imports) | ↑ (Increase in Imports) |
| Export Total | ↓ (Decrease in Exports) | ↑ (Increase in Exports) |
My analysis ¶
Import :
Stronger yen change the cost of imported goods, so for the same quantity of goods the price is lower, which will decrease the amount spent on import.
Weaker yen : the imported goods become more expensive and so for the same quantity the price is higher which increase the amount spent on import.
Following the inverse correlation
Export
Stronger yen : for foreigners the price of goods is higher so they will import less goods because japanese goods are less competitive. This leads to a decrease in demand for Japanese exports, potentially reducing the total export value.
Weaker yen : Japanese goods becomes cheaper so for foreigners buyers they can buy more of the same goods for the same price.This price advantage can boost demand for Japanese exports, increasing both the volume and the total value of exports.
Which also follow the inverse correlation
Explanation for the results ¶
From the JapanTimes (https://www.japantimes.co.jp/business/2024/05/22/economy/japan-trade-deficit-weak-yen-impact/)
"Imports gained 8.3% from a year ago, the Finance Ministry reported Wednesday, compared with the consensus estimate of an 8.9% increase."
"Exports advanced 8.3%, compared with the consensus of an 11% increase."
"While the weak yen has helped boost earnings for exporters such as Toyota, it has also driven up costs of imports of everything from fuel and food to raw materials needed for manufacturing."
White Paper on International Economy and Trade 2022
"Japan’s terms of trade deteriorated significantly also due to currency depreciation
with balance of payments under pressure due to increasing commodity prices,
imports of medical goods (vaccines), and a sharp decline in international tourists." p12
"However, Japan mainly imports energy-related goods from Russia and is highly dependent, if not more highly than some European countries, on some goods (7.4% of LNG, 10.2% of coal, and 3.7% of crude oil)." p18
White Paper on International Economy and Trade 2023
"Improve the trade balance by promoting exports
・ The pandemic, high resource prices, and a weak yen
have caused the current trade deficit to be the
largest in history" p22
"The record-high trade deficit was brought on by surging import prices for fossil fuels. Lowering dependency on imported fossil fuels is an important task for a resilient trade structure." p23
Japan Import Volume 2015 = 100% https://data.worldbank.org/indicator/TM.QTY.MRCH.XD.WD?skipRedirection=true&type=shaded&view=map&year=2021
To improve and verify our hypothesis we are going to take a look at the trade volume for Imports, and see if it change a lot or not. Where we could be able to conclude if the amount of trade value for the import is set by the exchange rate.
The data here are annualy, we will transform them to quaterly one, to have a lot more 'points' to look at, and also because the other two variables are quarterly.
Analysis of JXY Value, Import Trade Value, and Import Volume in Japan¶
The provided graph displays the changes in JXY (assumed to be a price index or currency value indicator), Import Trade Value, and Import Volume in Japan, normalized to 2015 values (indexed at 100). The data spans several years, showing significant fluctuations and trends that help explain the observed negative correlation between the JXY value and import trade value.
Graph Analysis¶
The graph clearly illustrates the relative stability in the volume of imports over time, despite fluctuations in the JXY value. This stability in import volume is critical in understanding the economic impact of changes in the yen's value on the total import trade value.
Impact of Yen Strength on Import Trade Value Stronger Yen: When the yen strengthens, the cost of imported goods measured in foreign currencies decreases. Despite the import volume remaining stable, as seen in the graph, the total expenditure on these imports in yen decreases. This is because each unit of foreign goods costs fewer yen due to the stronger currency. This aligns with the observed periods where the JXY value is high but the total import value dips, confirming the inverse relationship between yen strength and import expenditure.
Weaker Yen: Conversely, a weaker yen leads to an increase in the cost per unit of imported goods. For the same stable quantity of imports, the value of imports measured in yen increases. This is visible in the graph during periods where the JXY value decreases, and the total import trade value shows an uptick. The higher cost per unit translates into a higher total expenditure on imports, despite no significant change in the volume of goods imported.
Conclusion This graph validates the inverse correlation between the yen's value and the amount spent on imports. The consistency in import volume highlights how currency fluctuations directly influence import costs and expenditures, demonstrating a clear example of how exchange rate movements can impact national economic indicators.
Chapter 5 - Import and Export details ¶
Datas are from : https://oec.world/en/profile/country/jpn?yearSelector1=2022
The data have been put together manually
Exports ¶
| Date | Section | Subsection | Type | Trade Value | |
|---|---|---|---|---|---|
| 0 | 1995 | Animal Products | Live animals | Horses | 3281336.0 |
| 1 | 1995 | Animal Products | Live animals | Bovine | 7847428.0 |
| 2 | 1995 | Animal Products | Live animals | Pigs | 4265.0 |
| 3 | 1995 | Animal Products | Live animals | Sheep and Goats | 12684.0 |
| 4 | 1995 | Animal Products | Live animals | Poultry | 349833.0 |
| ... | ... | ... | ... | ... | ... |
| 34297 | 2022 | Arts and Antiques | Art & antiques | Prints | 19057413.0 |
| 34298 | 2022 | Arts and Antiques | Art & antiques | Sculptures | 77700811.0 |
| 34299 | 2022 | Arts and Antiques | Art & antiques | Revenue Stamps | 1112910.0 |
| 34300 | 2022 | Arts and Antiques | Art & antiques | Collector's Items | 26779364.0 |
| 34301 | 2022 | Arts and Antiques | Art & antiques | Antiques | 24452766.0 |
34302 rows × 5 columns
Correlation with JXY:
The correlation between the JXY index and the export values of these sectors generally supports the view that a weaker yen (higher JXY value) enhances export performance. Most sectors show improved trade values when the JXY index is low, suggesting increased competitiveness in international markets.
The relationship is particularly strong for sectors like Chemical Products, Precious Metals, and Metals, which have high export value growth when the yen is weaker.
Economic Implications:
Exchange Rate Sensitivity: The export performance of these top sectors indicates a high sensitivity to exchange rate movements. As the yen depreciates, Japanese goods become cheaper and more attractive in foreign markets, boosting exports.
Sectoral Strategies: Sectors showing significant growth and responsiveness to a weaker yen might focus on leveraging exchange rate movements to enhance their export strategies. Conversely, sectors with less responsiveness or declining trends might need to diversify markets, improve product innovation, or increase efficiency to remain competitive.
Policy Considerations: Policymakers need to consider the exchange rate impacts when devising trade and industrial policies. Supporting sectors that show strong export potential and are highly sensitive to yen depreciation can drive overall economic growth.
Correlation between Yen and Export Sectors: Price 1.000000 Stone And Glass 0.771223 Textiles 0.624630 Animal Hides 0.584089 Metals 0.578608 Plastics and Rubbers 0.546862 Machines 0.488134 Transportation 0.416009 Mineral Products 0.102426 Paper Goods 0.089103 Vegetable Products 0.053591 Precious Metals 0.049369 Instruments -0.031676 Miscellaneous -0.033135 Chemical Products -0.088200 Footwear and Headwear -0.271213 Weapons -0.367360 Animal and Vegetable Bi-Products -0.457197 Foodstuffs -0.473827 Arts and Antiques -0.596389 Date -0.600838 Animal Products -0.642820 Wood Products -0.654932 Name: Price, dtype: float64
Summary of Correlation between JXY and Export Sectors¶
The correlation analysis between the Japanese Yen Index (JXY) and various export sectors highlights diverse sensitivities to currency fluctuations. Notably, sectors such as Stone and Glass (0.771223), Textiles (0.624630), and Metals (0.578608) exhibit strong positive correlations with the JXY, indicating that these sectors benefit significantly from a weaker yen. As the yen depreciates, these sectors see improved competitiveness and higher export values, reflecting their reliance on favorable exchange rate movements.
Conversely, several sectors show negative correlations with the JXY, suggesting they are adversely affected by a weaker yen. For instance, sectors like Animal Products (-0.642820), Wood Products (-0.654932), and Arts and Antiques (-0.596389) see reduced export performance with yen depreciation. These sectors might depend more on import components or face stiff competition globally, making them less resilient to currency depreciation.
Exports Detailed ¶
C:\Users\bodin\AppData\Local\Temp\ipykernel_10956\4094174606.py:2: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
Correlation between 'Price' and 'Trade Value' for each type of machine: Type Engine Parts 0.765058 Combustion Engines 0.725811 Semiconductor Devices 0.648683 Air Pumps 0.563180 Industrial Printers 0.518694 Transmissions 0.488371 Computers 0.482420 Ball Bearings 0.453397 Broadcasting Accessories 0.439632 Telephones 0.412487 Broadcasting Equipment 0.317311 Low-voltage Protection Equipment 0.254983 Video Recording Equipment 0.054295 Large Construction Vehicles 0.028477 Integrated Circuits -0.026895 Machinery Having Individual Functions -0.040560 Spark-Ignition Engines -0.055814 Office Machine Parts -0.319780 Electrical Capacitors -0.376629 Electric Batteries -0.421871 dtype: float64
Analysis of the Machine Sector¶
Key Observations:
Correlation and Price Sensitivity: The correlation between the JXY and trade value for various machine types like Combustion Engines and Engine Parts is notably positive (e.g., 0.76588 for Engine Parts), suggesting that as the yen strengthens, the trade value of these products tends to increase. This could imply that these products hold a premium status and maintain demand despite price increases.
Scatter Plot Analysis:
The scatter plot shows a wide spread in trade values across different price points for items like Engine Parts and Combustion Engines, indicating a varied sensitivity to price changes. Products like Integrated Circuits and Electrical Capacitors, however, show a negative correlation, suggesting that higher prices might deter export volumes, possibly due to the availability of cheaper alternatives.
Economic Interpretation:
High-Value and Technological Products: Higher correlations for items like Semiconductor Devices and Engine Parts may reflect their critical nature and lesser price elasticity of demand. These are typically higher-value products where buyers may be less sensitive to price changes due to the specialized nature of the products.
Negative Correlation Products: Products such as Integrated Circuits showing a negative correlation could be facing stiff global competition, making price increases a disadvantage for Japanese exports in these categories.
C:\Users\bodin\AppData\Local\Temp\ipykernel_10956\3912211246.py:2: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
Correlation between 'Price' and 'Trade Value' for each type of Transportation: Type Passenger and Cargo Ships 0.914847 Scrap Vessels 0.766403 Motor vehicle (8701 to 8705) chassis fitted with engine 0.718099 Locomotive Parts 0.553611 Buses 0.512165 Recreational Boats 0.489423 Motor vehicles; parts and accessories (8701 to 8705) 0.472727 Special purpose motor vehicles 0.471035 Delivery Trucks 0.406695 Special Purpose Ships 0.227893 Other Sea Vessels 0.205035 Motorcycles and cycles 0.069373 Bi-Wheel Vehicle Parts 0.026318 Tractors 0.022238 Aircraft Parts -0.021346 Cars -0.075348 Planes, Helicopters, and/or Spacecraft -0.154974 Vehicle Bodies (including cabs) for the motor vehicles (8701 to 8705) -0.173756 Self-Propelled Rail Transport -0.249777 Railway Passenger Cars -0.491479 dtype: float64
Analysis of the Transportation Sector¶
Key Observations:
Correlation and Price Sensitivity: The transportation sector shows very high positive correlations for items like Passenger and Cargo Ships (0.914847) and Motor vehicle chassis fitted with engines (0.758199), indicating strong demand persists despite price increases possibly due to the JXY's strength.
Cars: Display a near-zero correlation (-0.075348), suggesting that price increases have a minimal direct impact on trade volumes. This could indicate a highly competitive market where consumers have numerous alternatives, possibly diluting the impact of price changes due to currency fluctuations.
Scatter Plot Analysis:
Larger items such as Passenger and Cargo Ships and Motor vehicle chassis show higher trade values at higher price points, indicating robust demand. Conversely, products like Self-Propelled Rail Transport and Railway Passenger Cars show significant negative correlations and lower trade values, suggesting price hikes may severely impact their competitiveness.
Cars and Motorcycles: The scatter plots for cars and motorcycles show trade values distributed across a range of prices with no evident trend of increasing trade value with higher prices, supporting their observed negative correlations.
Economic Interpretation:
Capital-Intensive Products: The strong positive correlations for big-ticket items like ships and vehicle chassis suggest that these capital-intensive products may not easily be substituted and buyers might absorb the cost increases due to the yen's appreciation.
Competitive and Price-Sensitive Products: Negative correlations in rail-related products may indicate high competition in these markets. The sensitivity to the yen's price may reflect the global nature of these markets, where buyers have multiple suppliers to choose from, pressuring Japanese exporters to maintain competitive pricing.
Cars: The correlation close to zero for cars highlights their position in a highly competitive and saturated market where even minor price fluctuations may not significantly alter trade volumes. This suggests that other factors such as brand loyalty, specific car features, and broader economic conditions may play more critical roles in influencing car sales than mere price changes induced by currency fluctuations.
General Conclusion¶
In sectors analyzed, the response to the yen's valuation appears sector and product-specific. High-technology and capital-intensive exports from Japan exhibit a resilient demand pattern, less affected by yen appreciation, likely due to the specialized nature of these products and their critical importance in global supply chains. Conversely, more commoditized products facing global competition show greater price sensitivity, where yen appreciation could negatively impact export volumes. This nuanced view suggests that currency management and pricing strategies must be finely tuned to the characteristics of each export sector to optimize Japan's trade balance.
Imports ¶
| Date | Section | Subsection | Type | Trade Value | |
|---|---|---|---|---|---|
| 0 | 1995 | Animal Products | Live animals | Horses | 188082338.0 |
| 1 | 1995 | Animal Products | Live animals | Bovine | 11391513.0 |
| 2 | 1995 | Animal Products | Live animals | Pigs | 1792014.0 |
| 3 | 1995 | Animal Products | Live animals | Sheep and Goats | 39113.0 |
| 4 | 1995 | Animal Products | Live animals | Poultry | 11194488.0 |
| ... | ... | ... | ... | ... | ... |
| 34406 | 2022 | Arts and Antiques | Art & antiques | Prints | 18368692.0 |
| 34407 | 2022 | Arts and Antiques | Art & antiques | Sculptures | 44203482.0 |
| 34408 | 2022 | Arts and Antiques | Art & antiques | Revenue Stamps | 788471.0 |
| 34409 | 2022 | Arts and Antiques | Art & antiques | Collector's Items | 63824562.0 |
| 34410 | 2022 | Arts and Antiques | Art & antiques | Antiques | 45423519.0 |
34411 rows × 5 columns
Sectoral Performance:
Animal Products: This sector shows significant growth in normalized trade value, particularly towards the latter part of the period. The strong growth may be driven by increasing demand for imported animal products, possibly due to limitations in domestic supply or rising consumer preference for foreign products.
Mineral Products: Exhibiting consistent growth, mineral products align with the trend of a weaker yen, as Japan relies heavily on imports for energy and raw materials. The depreciation of the yen raises the cost of these imports, which might explain the increased trade value.
Chemical Products and Machines: These sectors display robust growth, closely following the JXY index's movement. The correlation suggests that as the yen weakens, the cost of importing these essential industrial inputs rises, thereby increasing the normalized trade value.
Textiles and Instruments: These sectors show more volatility but generally increase over the period. The yen's fluctuations significantly impact these sectors, which rely on both raw materials and finished goods imports.
Transportation and Foodstuffs: These sectors demonstrate moderate growth with noticeable dips and recoveries, indicating responsiveness to economic cycles and exchange rate movements.
Economic Implications:
Exchange Rate Sensitivity: The import performance of these top sectors indicates a high sensitivity to exchange rate movements. A weaker yen generally increases the cost of imports, reflecting higher trade values in sectors like mineral products and animal products, which are crucial for domestic consumption and production.
Sectoral Strategies: Sectors showing significant growth and responsiveness to a weaker yen might need to focus on cost management and efficiency improvements to mitigate the impact of higher import costs. Diversifying sources and investing in domestic alternatives could also be beneficial.
Correlation between Yen and Imports Sectors: Price 1.000000 Paper Goods 0.731489 Mineral Products 0.492318 Vegetable Products 0.250296 Stone And Glass 0.237437 Weapons 0.221621 Textiles 0.212235 Wood Products 0.049506 Animal Products 0.020189 Foodstuffs -0.023347 Metals -0.075490 Footwear and Headwear -0.094885 Plastics and Rubbers -0.106273 Animal and Vegetable Bi-Products -0.184162 Chemical Products -0.285420 Animal Hides -0.307379 Precious Metals -0.311729 Miscellaneous -0.335057 Instruments -0.343810 Machines -0.415218 Transportation -0.477334 Arts and Antiques -0.578512 Date -0.600838 Name: Price, dtype: float64
Summary of Correlation and model¶
The correlation analysis between the Japanese Yen Index (JXY) and various import sectors reveals diverse sensitivities to currency fluctuations. Notably, Paper Goods (0.731489) and Mineral Products (0.492318) exhibit strong positive correlations with the JXY, indicating that these sectors see increased import values when the yen depreciates. This reflects Japan's reliance on imported paper goods and mineral products, which become more expensive with a weaker yen.
Conversely, several sectors show negative correlations with the JXY, suggesting they are less affected or even benefit from a stronger yen. For example, sectors such as Chemical Products (-0.285420), Machines (-0.415218), and Transportation (-0.477334) display significant negative correlations. These sectors may benefit from a stronger yen, which reduces the cost of importing essential industrial and manufacturing inputs, thereby lowering overall trade values in yen terms.
Imports Detailed ¶
Correlation between 'Price' and 'Trade Value' for each type of machine: Type Broadcasting Accessories 0.811752 Telephones 0.672818 Industrial Printers 0.578634 Video Displays 0.514311 Computers 0.432012 Air Conditioners 0.104986 Video Recording Equipment -0.052923 Semiconductor Devices -0.156901 Electric Heaters -0.241466 Air Pumps -0.293024 Electric Motors -0.305125 Electrical Transformers -0.436433 Machinery Having Individual Functions -0.478814 Office Machine Parts -0.501690 Other Electrical Machinery -0.510923 Insulated Wire -0.525610 Broadcasting Equipment -0.550635 Gas Turbines -0.576757 Low-voltage Protection Equipment -0.584278 Integrated Circuits -0.588212 dtype: float64
Analysis of the Machine Sector¶
Observations:
The machine sector exhibits a mix of positive and negative correlations between the price of the yen (JXY) and trade value.
High positive correlations are evident for items like Broadcasting Accessories (0.811752) and Telephones (0.672818), suggesting that as the price increases, so does the trade value, likely due to the specialized nature and low elasticity of demand for these products.
Economic Interpretation: Specialized Equipment: Products like Broadcasting Accessories and Telephones often represent specialized inputs necessary for specific industries, possibly explaining why higher prices do not deter import volumes significantly. Negative Correlations: Items like Integrated Circuits (-0.588212) show a strong negative correlation, suggesting that price increases could lead to a significant drop in import values, possibly due to available alternatives or competitive global production capacities.
Hypothesis for Specific Observations
Integrated Circuits:
The strong negative correlation observed for Integrated Circuits may reflect their critical role in various electronic products where price sensitivity is heightened by global supply chain alternatives and competitive pricing strategies. As prices increase, buyers may opt for cheaper alternatives from other global suppliers, reducing the import value of these circuits into Japan.
Computers:
Despite being a major import, the data on Computers (not explicitly shown in the given data but typically following similar trends to Integrated Circuits) likely exhibits similar price sensitivity. Computers are commoditized products with many global manufacturers, allowing for significant consumer choice and sensitivity to price changes. This sensitivity is exacerbated by the yen's fluctuation, which directly affects the import cost and hence the trade value.
Additional Analysis:
The scatter plot further illustrates these dynamics, where items like Gas Turbines and Air Pumps, shown at higher trade values despite varied prices, indicate less price sensitivity compared to more commoditized products like Integrated Circuits and Computers. These dynamics suggest that the elasticity of demand significantly influences how price changes impact trade values, with more specialized or essential items exhibiting less sensitivity to price changes driven by currency fluctuations.
Correlation between 'Price' and 'Trade Value' for each type of Minerals: Type Lead Ore 0.693273 Petroleum Coke 0.614992 Crude Petroleum 0.584005 Iron Ore 0.556012 Refined Petroleum 0.496364 Coal Tar Oil 0.435444 Manganese Ore 0.370756 Petroleum Gas 0.364756 Copper Ore 0.280925 Salt 0.165310 Titanium Ore 0.115616 Kaolin 0.085539 Nickel Ore 0.076063 Precious Metal Ore 0.034185 Molybdenum Ore -0.018362 Magnesium Carbonate -0.066365 Coal Briquettes -0.086231 Non-Iron and Steel Slag, Ash and Residues -0.104919 Coke -0.322668 Zinc Ore -0.557998 dtype: float64
Analysis of the Mineral Sector¶
Observations:
Correlations vary widely across different types of minerals. High positive correlations are found for commodities like Lead Ore (0.639273) and Crude Petroleum (0.584005), indicating that price increases are associated with higher import values.
Negative correlations are observed in commodities like Zinc Ore (-0.557998), where higher prices may reduce import volumes due to substitution effects or domestic availability.
Economic Interpretation:
Commodity Dependence: The high correlation in essential commodities like Crude Petroleum reflects Japan's dependency on imports for these resources, making them less sensitive to price changes.
Substitutable Minerals: Minerals like Zinc Ore showing negative correlations could indicate that Japan might source these minerals from alternative suppliers or substitute them with other materials, especially when prices rise.
Analysis of the Mineral Sector with a Strong Yen¶
The data indicate that as the yen strengthens (higher JXY values), Japan's import spending on commodities like Crude Petroleum (correlation of 0.584005) increases. This is contrary to common expectations where a stronger currency typically reduces the local currency cost of dollar-denominated commodities.
Economic Interpretation:
Reduced Cost and Increased Purchasing Power: When the yen is strong, it gains more value against other currencies, particularly the U.S. dollar, which is the standard currency for global oil trading. This increased purchasing power allows Japan to buy more for less, effectively reducing the cost per unit of imported goods like crude petroleum and natural gas.
Stockpiling and Strategic Purchases: With a stronger yen, Japan might strategically increase imports to build reserves or take advantage of favorable pricing, especially in commodities that are crucial for its energy security. This behavior can lead to a temporary increase in import volumes and values even as prices per unit might be lower.
Hypothesis:
Crude Petroleum and Natural Gas: The hypothesis is that with a stronger yen, Japan increases its imports of these essential commodities not just because they are cheaper but also to capitalize on the stronger currency to enhance national reserves or secure long-term contracts at favorable rates. This strategic behavior can explain why import values rise as the yen strengthens—Japan is effectively using its enhanced purchasing power to secure economic stability and energy security.
Further Considerations:
Global Market Conditions: The global oil and gas markets are highly volatile and can be influenced by geopolitical events, supply disruptions, and changes in global demand. A stronger yen provides Japan with an economic cushion to navigate this volatility more effectively by allowing for more aggressive purchasing when global prices are advantageous.
Energy Dependency: Japan's heavy reliance on imported energy due to limited domestic resources compels it to maximize favorable currency conditions to ensure steady energy supplies. This dependency makes it more responsive to currency fluctuations in its import strategy.
Correlation between 'Price' and 'Trade Value' for each type of Animals goods: Type Non-fillet Fresh Fish 0.821145 Fish: dried, salted, smoked or in brine 0.730493 Molluscs 0.688481 Crustaceans 0.657919 Non-fillet Frozen Fish 0.614418 Bird Feathers and Skins 0.405146 Live Fish 0.385402 Concentrated Milk 0.137301 Pig Meat 0.133164 Animal Organs -0.016551 Poultry Meat -0.059729 Fish Fillets -0.306688 Cheese -0.352499 Frozen Bovine Meat -0.368315 Whey and other milk products -0.411590 Sheep and Goat Meat -0.416866 Horses -0.436043 Honey -0.459663 Bovine Meat -0.514416 Edible Offal -0.669006 dtype: float64
Analysis of Animal Goods Imports¶
Observations:
The correlation and trade data for different types of animal goods reveal varied responses to changes in the yen's strength, represented by the JXY index. Products like Non-fillet Fresh Fish and Fish: dried, salted, smoked, or in brine show strong positive correlations with the JXY, indicating that their import spending increases as the yen strengthens. On the other hand, items such as Edible Offal and Bovine Meat exhibit strong negative correlations, where a stronger yen correlates with decreased spending.
Economic Interpretation:
Essential vs. Non-Essential Goods: Essential items, such as specific fish types, which demonstrate a positive correlation, may have inelastic demand characteristics. This suggests that imports continue or increase even as the yen strengthens and prices effectively decrease in foreign currency terms. These goods are likely critical, with limited local substitutes.
Price Sensitivity and Substitution: Non-essential goods or those with available substitutes show negative correlations. Importers might defer purchasing these items or seek alternative sources when their cost in yen increases, despite a stronger yen making imports nominally cheaper if priced in foreign currencies.
Hypotheses for Import Behavior Variations:
Volume Adjustments: For essential goods with positive correlations, the import volume might be maintained or increased as the yen strengthens, utilizing the currency advantage to purchase more at a lower relative cost. Conversely, for goods with negative correlations, volumes might decrease as buyers opt for local substitutes or delay purchases expecting better prices.
Strategic Purchasing: A stronger yen may lead importers of essential goods to stockpile, anticipating future price adjustments or shortages. This strategic behavior is less likely with non-essential goods, where purchasing can be more price and timing flexible.
Demand Elasticity: Essential goods typically show less sensitivity to price changes, resulting in steady or increased import volumes regardless of yen fluctuations. Non-essential goods, conversely, might see reduced imports as businesses and consumers adjust spending in response to the economic context provided by the yen's value.
General Conclusion¶
The correlations between the yen's price and trade values in these sectors illustrate how economic, strategic, and consumption patterns influence import behaviors. Specialized machinery and essential minerals or food items often show less price sensitivity, reflecting their critical role in Japan's economy and lifestyle. Conversely, more substitutable goods or those with viable local alternatives exhibit greater price sensitivity, indicating potential shifts towards local sourcing or alternative solutions when import prices rise. These insights highlight the importance of understanding sector-specific dynamics when analyzing trade patterns and making economic or policy decisions related to imports.
Chapter 6 - Tourism ¶
Another import point we can study with the exchage rate of different currencies is the amount of money spent by tourists during their stay in Japan. We can hypothesize the weaker the JPY, the more tourists will spend in Japan, which would be beneficial for tourism-related businesses.
For that, we can find data from the Japan National Tourism Organization, which concern around 15 countries, and provides us information about how much tourists spend. The downside is that we have only one data per year, so the fluctutions will be strongly undervalued if we compare it to the echange rate for which we have data every day. Also, due to the Covid pandemic, we do not have data for 2020, 2021 and 2022.
The first observation we can make here is that for every country, there was a great raise in the amount of money spent. It would however be hard to determine how much of this is due to the changes in convertion rate, because the global pandemic happened in this timeframe, and it changed people's behaviour. For example, we can hypothesize that people have been saving money and could spend more. Also, as we do not have data between 2019 and 2023, so it is hard to know the evolution of the curve, even if tourism in Japan was restricted during this period.
However, we can still observe some interesting facts:
- The two curves appear to be the most correlated for asian countries (Taiwan, Singapore, China, Hong Kong), but South Korea has surprisingly one of the less correlated curve.
- Countries that share the same currency (Germany and France) have very similar curves.
- For almost all currencies, the JPY began its fall in 2020 during the pandemic
It is important to think about other factors that can influence the amount of money spent by tourists. To try to understand better the dynamics, we will add to our graphs the evolution of the real GDP per capita of Germany, United Kingdom, Australia, Canada, South Korea, France and United States. These are the countries we can find data on the oecd website.
And as we expected, the real GDP is also very correlated to the evolution of the travel spendings. It is however important to notice that we do not have data here for countries such as China, Taiwan or Singapore, which were the ones that seemed to depent the most about the exchange rates.
Conclusion¶
It is difficult to explicit how much of an impact the exchange rates of different currencies have on the travel spendings. By adding a control variable, here the evolution of the real GDP, we observed that the travel spendings depend on more than one factor, and that its impact is different for separate countries (e.g. for Russia the impact is much less important than for Taiwan). That can also be a cause of stronger inequalities in these countries, but it is not something that will be researched further here.
Still, the impact of the exchange rate is not negligible, and in every case a weaker JPY implies more money spent by the tourists, and a stronger JPY implies less money spent by the tourists.
Chapter 7 - Production of Goods ¶
| Date | Mining and manufacturing | Manufacturing | Iron, steel and Non-ferrous metals | Iron and steel | Iron and steel crude products | Hot rolled steel | Cold finished steel | Steel pipes and tubes | Metallic coated steel | ... | Furniture | Printing | Rubber products | Other products | Watches and clocks | Instrument | Toys | Stationery | Leather goods | Mining | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 1978-01-01 | 63,1 | 62,9 | - | 114,9 | 117,7 | 109,2 | 96,2 | 218,4 | 84,5 | ... | 196,8 | - | 77,0 | 85,6 | 103,1 | 413,2 | 26,7 | 73,7 | 762,5 | 232,5 |
| 2 | 1978-02-01 | 66,4 | 66,1 | - | 112,6 | 106,4 | 107,3 | 94,5 | 216,5 | 83,3 | ... | 231,5 | - | 80,9 | 85,0 | 108,4 | 390,0 | 28,3 | 72,5 | 891,1 | 233,9 |
| 3 | 1978-03-01 | 74,6 | 74,3 | - | 121,3 | 117,2 | 113,4 | 102,2 | 237,9 | 86,2 | ... | 255,5 | - | 90,1 | 91,5 | 109,3 | 413,6 | 30,4 | 79,6 | 961,7 | 254,9 |
| 4 | 1978-04-01 | 72,0 | 71,9 | - | 117,6 | 117,2 | 111,0 | 99,5 | 213,6 | 87,3 | ... | 239,8 | - | 86,9 | 92,7 | 105,3 | 436,0 | 32,3 | 72,7 | 967,6 | 225,3 |
| 5 | 1978-05-01 | 70,2 | 70,1 | - | 121,5 | 120,7 | 114,6 | 104,7 | 222,4 | 92,8 | ... | 220,0 | - | 85,1 | 88,9 | 106,2 | 399,3 | 30,6 | 75,7 | 916,6 | 229,1 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 536 | 2022-08-01 | 100,8 | 100,8 | 101,4 | 104,7 | 106,0 | 106,9 | 103,0 | 95,9 | 100,3 | ... | 93,7 | 89,6 | 95,9 | 113,6 | 147,6 | 97,8 | 106,7 | 114,2 | 89,8 | 92,8 |
| 537 | 2022-09-01 | 112,1 | 112,2 | 108,9 | 108,2 | 102,3 | 104,4 | 110,6 | 102,5 | 104,3 | ... | 100,9 | 100,2 | 115,8 | 123,9 | 144,9 | 121,1 | 121,1 | 122,0 | 99,0 | 88,7 |
| 538 | 2022-10-01 | 105,4 | 105,4 | 110,0 | 110,1 | 105,0 | 110,1 | 109,1 | 102,4 | 109,5 | ... | 101,3 | 99,3 | 119,1 | 120,4 | 140,6 | 119,8 | 117,2 | 117,9 | 99,0 | 94,1 |
| 539 | 2022-11-01 | 108,6 | 108,7 | 110,2 | 109,8 | 102,8 | 108,5 | 108,3 | 104,7 | 103,9 | ... | 111,4 | 100,3 | 118,9 | 122,2 | 144,1 | 118,0 | 118,2 | 121,6 | 96,3 | 93,6 |
| 540 | 2022-12-01 | 107,6 | 107,7 | 103,2 | 102,6 | 99,4 | 101,2 | 101,4 | 95,4 | 101,4 | ... | 106,0 | 99,6 | 107,1 | 118,4 | 140,0 | 118,1 | 114,5 | 116,0 | 95,2 | 97,5 |
540 rows × 142 columns
Correlation between Yen and Production Sectors: Price 1.000000 Agricultural machinery 0.796738 Mining 0.789345 Chemical fertilizer 0.788793 Fabric 0.788448 Other ceramic products and stone products 0.782617 Textiles 0.740368 Wood and wood products 0.736167 Motorcycles 0.727292 Leather goods 0.720228 Dyeing and finishing processes 0.707384 Coal products 0.697088 Instrument 0.688143 Steel pipes and tubes 0.684725 Trucks 0.675915 Cement and cement products 0.543677 Furniture 0.542004 Aircraft parts 0.511116 Other production machinery 0.510315 Production machinery 0.509438 Semiconductor and flat-panel display manufacturing equipment 0.504161 Electronic circuit 0.482805 Equipment such as heating and cooking 0.457046 Other manufacturing 0.440474 Metal products of building 0.430625 Steel castings and forgings 0.419219 Fabricated structural metal products 0.418521 Tools for machines 0.417162 Ceramics, stone and clay products 0.414456 General-purpose, production and business oriented machinery (2010 version) 0.378391 Fabricated metals 0.320088 Analytical instruments and testing machines 0.319781 Aquatic food and vegetable food items 0.300553 Dairy products 0.257805 Noodles 0.255226 Chemicals (excl. Inorganic and organic chemicals) 0.230198 Office equipment 0.177689 Metal forming machinery 0.173911 Glass and glass products 0.168973 Daily lives industry machinery 0.168845 Electrical machinery 0.168356 General-purpose machinery 0.160246 Parts of general-purpose machinery 0.144492 Electrical rotating machinery 0.142012 Textile products and crude fiber products 0.137294 General-purpose and business oriented machinery 0.130696 Chemicals (excl. Inorganic, organic chemicals, and medicine) 0.109668 Ordinary steel (2010 version) 0.094449 Information and communication electronics equipment 0.094184 Business oriented machinery 0.092504 Plastic daily goods and containers 0.088200 Iron and steel crude products 0.086442 Switching devices 0.085631 Alcoholic beverages 0.074300 Seasoning 0.060792 Industrial vehicles 0.055535 Watches and clocks 0.050171 Machine for basic material industry 0.040770 Meat processed goods 0.017105 Milling and conditioning powder -0.000001 Iron and steel -0.010155 Refrigerating machines and appliances -0.018737 Special steel (2010 version) -0.020813 Electric measuring instrument -0.023012 Inorganic chemical industrial products -0.030070 Air conditioning and housing related equipment -0.032101 Sugar -0.044232 Edible fats and oils -0.045463 Bakery and confectionery -0.047457 Sintered products -0.050913 Transport equipment (excl. motor vehicles) -0.058869 Other industrial machinery -0.065496 Soft drink -0.066718 Wiring instruments, electric lamps and lighting fixtures -0.066730 Plastic foam products -0.077703 Foods and tobacco -0.084748 Boilers and power units -0.084849 Petroleum and coal products -0.085916 Industrial plastic products -0.092072 Information terminal device -0.092406 Plastic pipes, film, sheets and material for building -0.107037 Other organic chemical products -0.124026 Electrical machinery, and Information and communication electronics equipment -0.125932 Ceramics and ceramics related products -0.128309 Hot rolled steel -0.148926 Aliphatic intermediate -0.175063 Other metal products -0.176201 Motor vehicles -0.190833 Conveying equipment -0.195349 Non-ferrous metal rolled products -0.196516 Electronic parts and devices -0.197446 Housekeeping machine -0.217645 Pulp -0.217959 Car body and automobile parts -0.221573 Fiber -0.221843 Optical appliances and lens -0.222361 Iron, steel and Non-ferrous metals -0.226100 Consumer electronics -0.249202 Metal wire products -0.249956 Paints and printing inks -0.251484 Inorganic and organic chemicals -0.253151 Petrochemical base products -0.267018 Radio communication equipment -0.277985 Petroleum products -0.288124 Other products -0.289224 Cans -0.319358 Printing -0.374606 Wired communication equipment -0.377874 Construction and mining machinery -0.381937 Plastic products -0.382185 Non-ferrous metal refined and purified goods -0.453716 Stationery -0.455465 Cold finished steel -0.479447 Rubber products -0.521754 Ships and ship engines -0.525640 Integrated circuits -0.528244 Electric wires and cables -0.528983 Detergents and surfactants -0.529708 Toys -0.536684 Cosmetics -0.542786 Metallic coated steel -0.561886 Electronic application equipment -0.574021 Transport equipment -0.585091 Measuring machine and instruments -0.591948 Pumps and compressors -0.591964 Batteries -0.605240 Cyclic intermediate -0.606960 Plastic -0.651573 Electronic parts -0.655098 Electronic computer -0.657744 Passenger cars -0.665307 Paper -0.680852 Non-ferrous metal castings -0.684235 Electronic devices -0.691991 Manufacturing -0.704508 Mining and manufacturing -0.704554 Non-ferrous metals -0.705357 Date -0.737308 Paper processed goods -0.756686 Paperboard -0.786818 Pulp, paper and paper products -0.812121 Chemicals (excl. Medicine) -0.835358 Chemicals -0.872954 Name: Price, dtype: float64
Analysis and Interpretation¶
Positive Correlations¶
Agricultural Machinery (0.796738)¶
Agricultural machinery shows a strong positive correlation with the USD/Yen exchange rate (0.796738). This indicates that as the yen weakens against the USD, the production of agricultural machinery increases. A possible explanation for this could be that a weaker yen makes Japanese agricultural machinery more competitive in international markets, boosting exports. Additionally, the agricultural sector in Japan might increase investment in machinery to enhance productivity and offset higher costs of imported agricultural products.
Mining (0.789345)¶
The mining sector also exhibits a strong positive correlation (0.789345) with the USD/Yen exchange rate. A weaker yen likely makes Japanese mining exports more attractive globally, increasing production. Moreover, a weaker yen could raise the cost of importing raw materials and energy, encouraging domestic mining activities to reduce dependency on imports.
Chemical Fertilizer (0.788793)¶
The production of chemical fertilizers shows a significant positive correlation (0.788793) with the USD/Yen exchange rate. As the yen depreciates, the cost of importing raw materials for fertilizers increases, prompting domestic production to ramp up to meet local demand and reduce import reliance. Additionally, a weaker yen makes Japanese fertilizers more competitive in international markets, enhancing export opportunities.
Fabric (0.788448)¶
The fabric production sector's positive correlation (0.788448) with the USD/Yen exchange rate suggests that a weaker yen boosts domestic production. This could be due to increased demand for Japanese fabrics both domestically, as import substitution occurs due to higher prices of imported fabrics, and internationally, as Japanese fabrics become more competitively priced.
Negative Correlations¶
Chemicals (-0.872954)¶
Chemicals exhibit a strong negative correlation with the USD/Yen exchange rate (-0.872954). This suggests that as the yen appreciates, the production of chemicals decreases. An appreciated yen makes imported chemicals cheaper, reducing the need for domestic production. Additionally, the global competitiveness of Japanese chemical exports declines with a stronger yen, leading to lower production levels.
Pulp, Paper, and Paper Products (-0.812121)¶
The pulp, paper, and paper products sector shows a significant negative correlation (-0.812121) with the USD/Yen exchange rate. A stronger yen makes imported paper products more affordable, decreasing the demand for domestic production. Furthermore, the competitive disadvantage of Japanese paper products in international markets due to a stronger yen reduces export demand, leading to lower production.
Non-Ferrous Metals (-0.705357)¶
The non-ferrous metals sector also displays a strong negative correlation (-0.705357) with the USD/Yen exchange rate. An appreciated yen reduces the cost of importing non-ferrous metals, leading to a decrease in domestic production. The competitive disadvantage in exporting these metals when the yen is strong further lowers production levels.
Manufacturing (-0.704508)¶
Manufacturing overall shows a negative correlation (-0.704508) with the USD/Yen exchange rate. A stronger yen increases the cost of Japanese manufactured goods in global markets, reducing exports. Additionally, the affordability of imported manufactured goods rises with a stronger yen, further dampening domestic production.
Summary¶
The correlation analysis highlights distinct impacts of the USD/Yen exchange rate on various production sectors. Sectors such as agricultural machinery, mining, chemical fertilizers, fabrics, and ceramic products benefit from a weaker yen, which enhances their competitiveness in global markets and boosts domestic production to offset higher import costs. Conversely, sectors such as chemicals, pulp and paper products, paperboard, non-ferrous metals, and manufacturing are negatively impacted by a stronger yen, which reduces their global competitiveness and increases the attractiveness of imported goods.
These findings underscore the complex interplay between exchange rate movements and industrial output. Policymakers and business leaders must consider these dynamics when formulating strategies to mitigate risks and capitalize on opportunities presented by currency fluctuations. Understanding how different sectors respond to exchange rate changes can inform targeted interventions to enhance the resilience and competitiveness of the Japanese economy in the global marketplace.
Don't know yet how to use this part
| Sector | Export Correlation | Import Correlation | Production Correlation |
|---|---|---|---|
| Textiles | 0.624630 | 0.212235 | 0.740368 |
| Wood Products | -0.654932 | 0.049506 | 0.736167 (Wood and wood products) |
| Metals | 0.578608 | -0.075490 | -0.684725(steel pipes and tubes),0.320088 (frabricated metals) |
| Plastics and Rubbers | 0.546862 | -0.106273 | --0.382185 (plastic product) |
| Machines | 0.488134 | -0.415218 | 0.509438 (Production machinery),0.482805 (Electronics circuits),-0.691991 (electronic devices) |
| Transportation | 0.416009 | -0.477334 | -0.585091 (Transport equipment),0.727292 (motocycles) |
| Mineral Products | 0.102426 | 0.492318 | -0.704554 (Mining and manufacturing),0.697088(coal product), |
| Chemical Products | -0.088200 | -0.285420 | -0.835358 (Chemicals) |
| Instruments | -0.031676 | -0.343810 | 0.688143 |
| Foodstuffs | -0.473827 | -0.023347 | -0.255226 (nooddles) |
| Animal Hides | 0.584089 | -0.307379 | -0.720228 (leather goods) |
| Paper Goods | 0.089103 | 0.731489 | -0.812121 (Pulp, paper and paper products) |
| Vegetable Products | 0.053591 | 0.250296 | -0.300553 (vegetables food items) |
Comparative Correlation Analysis Across Sectors¶
Interpretation and Hypotheses¶
Cross-Sectoral Insights:
High Correlation in Exports and Low in Imports: Sectors like Textiles and Metals show strong positive export correlations, suggesting that a stronger yen enhances their international competitiveness, possibly due to branding or quality perception. Conversely, their import correlations are low, which might indicate sufficient domestic production capacity or strategic import substitution.
Negative Export Correlation with Positive Import Correlation: Foodstuffs and Wood Products exhibit this pattern, which could be due to the essential nature of these goods, making them less sensitive to price changes due to currency fluctuations.
Complex Machine and Transportation Sectors: These sectors show mixed correlations across datasets. For instance, Machines have positive correlations in exports and production but a negative in imports, highlighting Japan’s strong domestic machinery industry that competes effectively globally despite a strong yen. Conversely, Transportation has a negative correlation in both import and production but a positive in exports for Motorcycles, possibly indicating niche markets or specialized production that benefits from a strong yen.
Mineral and Chemical Products: These sectors show a stark contrast, with Mineral Products having a weak positive correlation in exports but a strong positive in imports, reflecting Japan's dependency on mineral imports like coal for energy and manufacturing needs. Chemical Products consistently show negative correlations, suggesting that these sectors are highly sensitive to price increases, potentially due to global competition and alternative sources.
Economic Hypotheses:
Strategic Import Substitution and Export Competitiveness: Sectors with strong domestic industries (like Machines and Metals) demonstrate resilience to yen appreciation, maintaining or even increasing export capacities. In contrast, sectors reliant on imports for raw materials (like Mineral and Paper Goods) may see increased costs that are not fully passed through to international markets.
Impact of Yen on Essential vs. Luxury Goods: Essential sectors (like Foodstuffs and Wood Products) might exhibit less sensitivity to yen fluctuations, maintaining import volumes to meet domestic needs. Conversely, luxury or non-essential sectors (like Arts and Antiques or high-end Machines) might see reduced activity as the yen strengthens, affecting both import costs and export competitiveness.
This analysis provides a nuanced view of how different sectors react to changes in the yen's value, influenced by factors like market positioning, domestic production capabilities, and global economic dynamics.
Found existing installation: jupyter-contrib-nbextensions 0.5.1 Uninstalling jupyter-contrib-nbextensions-0.5.1: Successfully uninstalled jupyter-contrib-nbextensions-0.5.1 Found existing installation: jupyter_nbextensions_configurator 0.6.4 Uninstalling jupyter_nbextensions_configurator-0.6.4: Successfully uninstalled jupyter_nbextensions_configurator-0.6.4
WARNING: Skipping jupyter-toc as it is not installed.